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"tutorial/statistical_inference/supervised_learning.rst", "tutorial/statistical_inference/unsupervised_learning.rst", "tutorial/text_analytics/working_with_text_data.rst", "unsupervised_learning.rst", "user_guide.rst", "versions.rst", "visualizations.rst", "whats_new.rst", "whats_new/_contributors.rst", "whats_new/older_versions.rst", "whats_new/v0.13.rst", "whats_new/v0.14.rst", "whats_new/v0.15.rst", "whats_new/v0.16.rst", "whats_new/v0.17.rst", "whats_new/v0.18.rst", "whats_new/v0.19.rst", "whats_new/v0.20.rst", "whats_new/v0.21.rst", "whats_new/v0.22.rst", "whats_new/v0.23.rst", "whats_new/v0.24.rst", "whats_new/v1.0.rst", "whats_new/v1.1.rst", "whats_new/v1.2.rst", "whats_new/v1.3.rst", "whats_new/v1.4.rst", "whats_new/v1.5.rst"], "titles": ["About us", "&lt;no title&gt;", "&lt;no title&gt;", "Examples based on real world datasets", "Time-related feature engineering", "Image denoising using kernel PCA", "Faces recognition example using eigenfaces and SVMs", "Model Complexity Influence", "Out-of-core classification of text documents", "Outlier detection on a real data set", "Prediction Latency", "Species distribution modeling", "Visualizing the stock market structure", "Lagged features for time series forecasting", "Compressive sensing: tomography reconstruction with L1 prior (Lasso)", "Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation", "Computation times", "Libsvm GUI", "Wikipedia principal eigenvector", "Biclustering", "Biclustering documents with the Spectral Co-clustering algorithm", "A demo of the Spectral Biclustering algorithm", "A demo of the Spectral Co-Clustering algorithm", "Computation times", "Calibration", "Probability calibration of classifiers", "Probability Calibration curves", "Probability Calibration for 3-class classification", "Comparison of Calibration of Classifiers", "Computation times", "Classification", "Plot classification probability", "Classifier comparison", "Recognizing hand-written digits", "Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification", "Linear and Quadratic Discriminant Analysis with covariance ellipsoid", "Computation times", "Clustering", "Adjustment for chance in clustering performance evaluation", "Demo of affinity propagation clustering algorithm", "Agglomerative clustering with and without structure", "Agglomerative clustering with different metrics", "Plot Hierarchical Clustering Dendrogram", "Compare BIRCH and MiniBatchKMeans", "Bisecting K-Means and Regular K-Means Performance Comparison", "Comparing different clustering algorithms on toy datasets", "K-means Clustering", "Segmenting the picture of greek coins in regions", "A demo of structured Ward hierarchical clustering on an image of coins", "Color Quantization using K-Means", "Demo of DBSCAN clustering algorithm", "Online learning of a dictionary of parts of faces", "Feature agglomeration", "Various Agglomerative Clustering on a 2D embedding of digits", "Vector Quantization Example", "Feature agglomeration vs. univariate selection", "Demo of HDBSCAN clustering algorithm", "Inductive Clustering", "Demonstration of k-means assumptions", "A demo of K-Means clustering on the handwritten digits data", "An example of K-Means++ initialization", "Selecting the number of clusters with silhouette analysis on KMeans clustering", "Empirical evaluation of the impact of k-means initialization", "Comparing different hierarchical linkage methods on toy datasets", "A demo of the mean-shift clustering algorithm", "Comparison of the K-Means and MiniBatchKMeans clustering algorithms", "Demo of OPTICS clustering algorithm", "Spectral clustering for image segmentation", "Hierarchical clustering: structured vs unstructured ward", "Computation times", "Pipelines and composite estimators", "Column Transformer with Heterogeneous Data Sources", "Column Transformer with Mixed Types", "Selecting dimensionality reduction with Pipeline and GridSearchCV", "Pipelining: chaining a PCA and a logistic regression", "Concatenating multiple feature extraction methods", "Effect of transforming the targets in regression model", "Computation times", "Covariance estimation", "Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood", "Ledoit-Wolf vs OAS estimation", "Robust covariance estimation and Mahalanobis distances relevance", "Robust vs Empirical covariance estimate", "Sparse inverse covariance estimation", "Computation times", "Cross decomposition", "Compare cross decomposition methods", "Principal Component Regression vs Partial Least Squares Regression", "Computation times", "Dataset examples", "The Digit Dataset", "The Iris Dataset", "Plot randomly generated classification dataset", "Plot randomly generated multilabel dataset", "Computation times", "Decomposition", "Faces dataset decompositions", "Blind source separation using FastICA", "FastICA on 2D point clouds", "Image denoising using dictionary learning", "Incremental PCA", "Kernel PCA", "PCA example with Iris Data-set", "Model selection with Probabilistic PCA and Factor Analysis (FA)", "Comparison of LDA and PCA 2D projection of Iris dataset", "Sparse coding with a precomputed dictionary", "Factor Analysis (with rotation) to visualize patterns", "Computation times", "Developing Estimators", "Computation times", "<code class=\"docutils literal notranslate\"><span class=\"pre\">__sklearn_is_fitted__</span></code> as Developer API", "Ensemble methods", "Multi-class AdaBoosted Decision Trees", "Decision Tree Regression with AdaBoost", "Two-class AdaBoost", "Single estimator versus bagging: bias-variance decomposition", "OOB Errors for Random Forests", "Feature transformations with ensembles of trees", "Comparing Random Forests and Histogram Gradient Boosting models", "Feature importances with a forest of trees", "Pixel importances with a parallel forest of trees", "Plot the decision surfaces of ensembles of trees on the iris dataset", "Categorical Feature Support in Gradient Boosting", "Early stopping in Gradient Boosting", "Gradient Boosting Out-of-Bag estimates", "Prediction Intervals for Gradient Boosting Regression", "Gradient Boosting regression", "Gradient Boosting regularization", "IsolationForest example", "Monotonic Constraints", "Hashing feature transformation using Totally Random Trees", "Comparing random forests and the multi-output meta estimator", "Combine predictors using stacking", "Plot the decision boundaries of a VotingClassifier", "Plot class probabilities calculated by the VotingClassifier", "Plot individual and voting regression predictions", "Computation times", "Tutorial exercises", "Cross-validation on diabetes Dataset Exercise", "Cross-validation on Digits Dataset Exercise", "Digits Classification Exercise", "SVM Exercise", "Computation times", "Feature Selection", "Comparison of F-test and mutual information", "Univariate Feature Selection", "Pipeline ANOVA SVM", "Recursive feature elimination", "Recursive feature elimination with cross-validation", "Model-based and sequential feature selection", "Computation times", "Gaussian Process for Machine Learning", "Comparison of kernel ridge and Gaussian process regression", "Probabilistic predictions with Gaussian process classification (GPC)", "Gaussian process classification (GPC) on iris dataset", "Iso-probability lines for Gaussian Processes classification (GPC)", "Illustration of Gaussian process classification (GPC) on the XOR dataset", "Forecasting of CO2 level on Mona Loa dataset using Gaussian process regression (GPR)", "Ability of Gaussian process regression (GPR) to estimate data noise-level", "Gaussian Processes regression: basic introductory example", "Gaussian processes on discrete data structures", "Illustration of prior and posterior Gaussian process for different kernels", "Computation times", "Missing Value Imputation", "Imputing missing values with variants of IterativeImputer", "Imputing missing values before building an estimator", "Computation times", "Examples", "Inspection", "Failure of Machine Learning to infer causal effects", "Common pitfalls in the interpretation of coefficients of linear models", "Partial Dependence and Individual Conditional Expectation Plots", "Permutation Importance vs Random Forest Feature Importance (MDI)", "Permutation Importance with Multicollinear or Correlated Features", "Computation times", "Kernel Approximation", "Scalable learning with polynomial kernel approximation", "Computation times", "Generalized Linear Models", "Comparing Linear Bayesian Regressors", "Curve Fitting with Bayesian Ridge Regression", "Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples", "HuberRegressor vs Ridge on dataset with strong outliers", "Logistic Regression 3-class Classifier", "L1-based models for Sparse Signals", "Lasso and Elastic Net", "Lasso on dense and sparse data", "Lasso path using LARS", "Lasso model selection via information criteria", "Lasso model selection: AIC-BIC / cross-validation", "Logistic function", "L1 Penalty and Sparsity in Logistic Regression", "Plot multinomial and One-vs-Rest Logistic Regression", "Regularization path of L1- Logistic Regression", "Joint feature selection with multi-task Lasso", "Non-negative least squares", "Linear Regression Example", "Sparsity Example: Fitting only features 1  and 2", "Ordinary Least Squares and Ridge Regression Variance", "Orthogonal Matching Pursuit", "Poisson regression and non-normal loss", "Polynomial and Spline interpolation", "Quantile regression", "Robust linear model estimation using RANSAC", "Ridge coefficients as a function of the L2 Regularization", "Plot Ridge coefficients as a function of the regularization", "Robust linear estimator fitting", "Comparing various online solvers", "Early stopping of Stochastic Gradient Descent", "Plot multi-class SGD on the iris dataset", "SGD: convex loss functions", "SGD: Penalties", "SGD: Maximum margin separating hyperplane", "SGD: Weighted samples", "One-Class SVM versus One-Class SVM using Stochastic Gradient Descent", "Multiclass sparse logistic regression on 20newgroups", "MNIST classification using multinomial logistic + L1", "Theil-Sen Regression", "Tweedie regression on insurance claims", "Computation times", "Manifold learning", "Comparison of Manifold Learning methods", "Manifold learning on handwritten digits: Locally Linear Embedding, Isomap\u2026", "Manifold Learning methods on a severed sphere", "Multi-dimensional scaling", "Swiss Roll And Swiss-Hole Reduction", "t-SNE: The effect of various perplexity values on the shape", "Computation times", "Miscellaneous", "Comparing anomaly detection algorithms for outlier detection on toy datasets", "Visualizations with Display Objects", "Displaying estimators and complex pipelines", "Isotonic Regression", "The Johnson-Lindenstrauss bound for embedding with random projections", "Explicit feature map approximation for RBF kernels", "Comparison of kernel ridge regression and SVR", "Metadata Routing", "Multilabel classification", "Face completion with a multi-output estimators", "Evaluation of outlier detection estimators", "Advanced Plotting With Partial Dependence", "Displaying Pipelines", "ROC Curve with Visualization API", "Introducing the <code class=\"docutils literal notranslate\"><span class=\"pre\">set_output</span></code> API", "Computation times", "Gaussian Mixture Models", "Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture", "Gaussian Mixture Model Ellipsoids", "GMM covariances", "GMM Initialization Methods", "Density Estimation for a Gaussian mixture", "Gaussian Mixture Model Selection", "Gaussian Mixture Model Sine Curve", "Computation times", "Model Selection", "Confusion matrix", "Visualizing cross-validation behavior in scikit-learn", "Plotting Cross-Validated Predictions", "Detection error tradeoff (DET) curve", "Custom refit strategy of a grid search with cross-validation", "Balance model complexity and cross-validated score", "Statistical comparison of models using grid search", "Sample pipeline for text feature extraction and evaluation", "Plotting Learning Curves and Checking Models\u2019 Scalability", "Class Likelihood Ratios to measure classification performance", "Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV", "Nested versus non-nested cross-validation", "Test with permutations the significance of a classification score", "Precision-Recall", "Comparing randomized search and grid search for hyperparameter estimation", "Multiclass Receiver Operating Characteristic (ROC)", "Receiver Operating Characteristic (ROC) with cross validation", "Comparison between grid search and successive halving", "Successive Halving Iterations", "Train error vs Test error", "Underfitting vs. Overfitting", "Plotting Validation Curves", "Computation times", "Multiclass methods", "Overview of multiclass training meta-estimators", "Computation times", "Multioutput methods", "Multilabel classification using a classifier chain", "Computation times", "Approximate nearest neighbors in TSNE", "Nearest Neighbors", "Caching nearest neighbors", "Nearest Neighbors Classification", "Kernel Density Estimation", "Simple 1D Kernel Density Estimation", "Novelty detection with Local Outlier Factor (LOF)", "Outlier detection with Local Outlier Factor (LOF)", "Comparing Nearest Neighbors with and without Neighborhood Components Analysis", "Dimensionality Reduction with Neighborhood Components Analysis", "Neighborhood Components Analysis Illustration", "Nearest Centroid Classification", "Nearest Neighbors regression", "Kernel Density Estimate of Species Distributions", "Computation times", "Neural Networks", "Varying regularization in Multi-layer Perceptron", "Compare Stochastic learning strategies for MLPClassifier", "Visualization of MLP weights on MNIST", "Restricted Boltzmann Machine features for digit classification", "Computation times", "Preprocessing", "Compare the effect of different scalers on data with outliers", "Using KBinsDiscretizer to discretize continuous features", "Feature discretization", "Demonstrating the different strategies of KBinsDiscretizer", "Map data to a normal distribution", "Importance of Feature Scaling", "Comparing Target Encoder with Other Encoders", "Target Encoder\u2019s Internal Cross fitting", "Computation times", "Release Highlights", "Release Highlights for scikit-learn 0.22", "Release Highlights for scikit-learn 0.23", "Release Highlights for scikit-learn 0.24", "Release Highlights for scikit-learn 1.0", "Release Highlights for scikit-learn 1.1", "Release Highlights for scikit-learn 1.2", "Release Highlights for scikit-learn 1.3", "Release Highlights for scikit-learn 1.4", "Computation times", "Semi Supervised Classification", "Label Propagation digits: Demonstrating performance", "Label Propagation digits active learning", "Label Propagation learning a complex structure", "Effect of varying threshold for self-training", "Semi-supervised Classification on a Text Dataset", "Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset", "Computation times", "Computation times", "Support Vector Machines", "SVM with custom kernel", "Plot different SVM classifiers in the iris dataset", "Plot the support vectors in LinearSVC", "One-class SVM with non-linear kernel (RBF)", "RBF SVM parameters", "SVM: Maximum margin separating hyperplane", "SVM: Separating hyperplane for unbalanced classes", "SVM-Anova: SVM with univariate feature selection", "Plot classification boundaries with different SVM Kernels", "SVM Margins Example", "Non-linear SVM", "Support Vector Regression (SVR) using linear and non-linear kernels", "Scaling the regularization parameter for SVCs", "SVM Tie Breaking Example", "SVM: Weighted samples", "Computation times", "Working with text documents", "Classification of text documents using sparse features", "Clustering text documents using k-means", "FeatureHasher and DictVectorizer Comparison", "Computation times", "Decision Trees", "Post pruning decision trees with cost complexity pruning", "Plot the decision surface of decision trees trained on the iris dataset", "Decision Tree Regression", "Multi-output Decision Tree Regression", "Understanding the decision tree structure", "Computation times", "<span class=\"section-number\">10. </span>Common pitfalls and recommended practices", "&lt;no title&gt;", "&lt;no title&gt;", "<span class=\"section-number\">8. </span>Computing with scikit-learn", "<span class=\"section-number\">8.2. </span>Computational Performance", "<span class=\"section-number\">8.3. </span>Parallelism, resource management, and configuration", "<span class=\"section-number\">8.1. </span>Strategies to scale computationally: bigger data", "Table Of Contents", "&lt;no title&gt;", "&lt;no title&gt;", "<span class=\"section-number\">6. </span>Dataset transformations", "<span class=\"section-number\">7. </span>Dataset loading utilities", "<span class=\"section-number\">7.4. </span>Loading other datasets", "<span class=\"section-number\">7.2. </span>Real world datasets", "<span class=\"section-number\">7.3. </span>Generated datasets", "<span class=\"section-number\">7.1. </span>Toy datasets", "Installing the development version of scikit-learn", "Bug triaging and issue curation", "Contributing", "Cython Best Practices, Conventions and Knowledge", "Developing scikit-learn estimators", "Developer\u2019s Guide", "Maintainer / core-developer information", "Crafting a minimal reproducer for scikit-learn", "How to optimize for speed", "Developing with the Plotting API", "Developers\u2019 Tips and Tricks", "Utilities for Developers", "<span class=\"section-number\">11. </span>Dispatching", "&lt;no title&gt;", "Frequently Asked Questions", "Getting Started", "Glossary of Common Terms and API Elements", "Scikit-learn governance and decision-making", "<span class=\"section-number\">4. </span>Inspection", "Installing scikit-learn", "<span class=\"section-number\">1. </span>Metadata Routing", "&lt;no title&gt;", "&lt;no title&gt;", "<span class=\"section-number\">9. </span>Model persistence", "<span class=\"section-number\">3. </span>Model selection and evaluation", "<span class=\"section-number\">11.1. </span>Array API support (experimental)", "<span class=\"section-number\">2.4. </span>Biclustering", "<span class=\"section-number\">1.16. </span>Probability calibration", "API Reference", "<span class=\"section-number\">2.3. </span>Clustering", "<span class=\"section-number\">6.1. </span>Pipelines and composite estimators", "<span class=\"section-number\">2.6. </span>Covariance estimation", "<span class=\"section-number\">1.8. </span>Cross decomposition", "<span class=\"section-number\">3.1. </span>Cross-validation: evaluating estimator performance", "<span class=\"section-number\">2.5. </span>Decomposing signals in components (matrix factorization problems)", "<span class=\"section-number\">2.8. </span>Density Estimation", "<span class=\"section-number\">1.11. </span>Ensembles: Gradient boosting, random forests, bagging, voting, stacking", "<span class=\"section-number\">6.2. </span>Feature extraction", "<span class=\"section-number\">1.13. </span>Feature selection", "<span class=\"section-number\">1.7. </span>Gaussian Processes", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cluster</span></code>.dbscan", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.decomposition</span></code>.fastica", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.covariance</span></code>.oas", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.base</span></code>.BaseEstimator", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.base</span></code>.BiclusterMixin", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.base</span></code>.ClassNamePrefixFeaturesOutMixin", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.base</span></code>.ClassifierMixin", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.base</span></code>.ClusterMixin", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.base</span></code>.DensityMixin", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.base</span></code>.MetaEstimatorMixin", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.base</span></code>.OneToOneFeatureMixin", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.base</span></code>.OutlierMixin", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.base</span></code>.RegressorMixin", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.base</span></code>.TransformerMixin", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.base</span></code>.clone", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.base</span></code>.is_classifier", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.base</span></code>.is_regressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.calibration</span></code>.CalibratedClassifierCV", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.calibration</span></code>.CalibrationDisplay", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.calibration</span></code>.calibration_curve", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cluster</span></code>.AffinityPropagation", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cluster</span></code>.AgglomerativeClustering", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cluster</span></code>.Birch", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cluster</span></code>.BisectingKMeans", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cluster</span></code>.DBSCAN", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cluster</span></code>.FeatureAgglomeration", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cluster</span></code>.HDBSCAN", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cluster</span></code>.KMeans", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cluster</span></code>.MeanShift", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cluster</span></code>.MiniBatchKMeans", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cluster</span></code>.OPTICS", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cluster</span></code>.SpectralBiclustering", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cluster</span></code>.SpectralClustering", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cluster</span></code>.SpectralCoclustering", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cluster</span></code>.affinity_propagation", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cluster</span></code>.cluster_optics_dbscan", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cluster</span></code>.cluster_optics_xi", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cluster</span></code>.compute_optics_graph", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cluster</span></code>.estimate_bandwidth", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cluster</span></code>.k_means", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cluster</span></code>.kmeans_plusplus", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cluster</span></code>.mean_shift", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cluster</span></code>.spectral_clustering", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cluster</span></code>.ward_tree", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.compose</span></code>.ColumnTransformer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.compose</span></code>.TransformedTargetRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.compose</span></code>.make_column_selector", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.compose</span></code>.make_column_transformer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn</span></code>.config_context", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.covariance</span></code>.EllipticEnvelope", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.covariance</span></code>.EmpiricalCovariance", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.covariance</span></code>.GraphicalLasso", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.covariance</span></code>.GraphicalLassoCV", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.covariance</span></code>.LedoitWolf", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.covariance</span></code>.MinCovDet", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.covariance</span></code>.OAS", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.covariance</span></code>.ShrunkCovariance", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.covariance</span></code>.empirical_covariance", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.covariance</span></code>.graphical_lasso", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.covariance</span></code>.ledoit_wolf", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.covariance</span></code>.ledoit_wolf_shrinkage", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.covariance</span></code>.shrunk_covariance", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cross_decomposition</span></code>.CCA", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cross_decomposition</span></code>.PLSCanonical", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cross_decomposition</span></code>.PLSRegression", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.cross_decomposition</span></code>.PLSSVD", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.clear_data_home", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.dump_svmlight_file", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.fetch_20newsgroups", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.fetch_20newsgroups_vectorized", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.fetch_california_housing", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.fetch_covtype", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.fetch_kddcup99", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.fetch_lfw_pairs", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.fetch_lfw_people", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.fetch_olivetti_faces", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.fetch_openml", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.fetch_rcv1", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.fetch_species_distributions", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.get_data_home", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.load_breast_cancer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.load_diabetes", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.load_digits", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.load_files", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.load_iris", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.load_linnerud", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.load_sample_image", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.load_sample_images", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.load_svmlight_file", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.load_svmlight_files", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.load_wine", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.make_biclusters", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.make_blobs", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.make_checkerboard", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.make_circles", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.make_classification", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.make_friedman1", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.make_friedman2", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.make_friedman3", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.make_gaussian_quantiles", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.make_hastie_10_2", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.make_low_rank_matrix", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.make_moons", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.make_multilabel_classification", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.make_regression", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.make_s_curve", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.make_sparse_coded_signal", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.make_sparse_spd_matrix", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.make_sparse_uncorrelated", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.make_spd_matrix", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.datasets</span></code>.make_swiss_roll", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.decomposition</span></code>.DictionaryLearning", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.decomposition</span></code>.FactorAnalysis", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.decomposition</span></code>.FastICA", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.decomposition</span></code>.IncrementalPCA", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.decomposition</span></code>.KernelPCA", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.decomposition</span></code>.LatentDirichletAllocation", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.decomposition</span></code>.MiniBatchDictionaryLearning", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.decomposition</span></code>.MiniBatchNMF", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.decomposition</span></code>.MiniBatchSparsePCA", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.decomposition</span></code>.NMF", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.decomposition</span></code>.PCA", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.decomposition</span></code>.SparseCoder", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.decomposition</span></code>.SparsePCA", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.decomposition</span></code>.TruncatedSVD", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.decomposition</span></code>.dict_learning", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.decomposition</span></code>.dict_learning_online", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.decomposition</span></code>.non_negative_factorization", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.decomposition</span></code>.sparse_encode", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.discriminant_analysis</span></code>.LinearDiscriminantAnalysis", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.discriminant_analysis</span></code>.QuadraticDiscriminantAnalysis", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.dummy</span></code>.DummyClassifier", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.dummy</span></code>.DummyRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.ensemble</span></code>.AdaBoostClassifier", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.ensemble</span></code>.AdaBoostRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.ensemble</span></code>.BaggingClassifier", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.ensemble</span></code>.BaggingRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.ensemble</span></code>.ExtraTreesClassifier", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.ensemble</span></code>.ExtraTreesRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.ensemble</span></code>.GradientBoostingClassifier", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.ensemble</span></code>.GradientBoostingRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.ensemble</span></code>.HistGradientBoostingClassifier", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.ensemble</span></code>.HistGradientBoostingRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.ensemble</span></code>.IsolationForest", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.ensemble</span></code>.RandomForestClassifier", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.ensemble</span></code>.RandomForestRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.ensemble</span></code>.RandomTreesEmbedding", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.ensemble</span></code>.StackingClassifier", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.ensemble</span></code>.StackingRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.ensemble</span></code>.VotingClassifier", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.ensemble</span></code>.VotingRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.exceptions</span></code>.ConvergenceWarning", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.exceptions</span></code>.DataConversionWarning", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.exceptions</span></code>.DataDimensionalityWarning", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.exceptions</span></code>.EfficiencyWarning", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.exceptions</span></code>.FitFailedWarning", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.exceptions</span></code>.InconsistentVersionWarning", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.exceptions</span></code>.NotFittedError", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.exceptions</span></code>.UndefinedMetricWarning", "sklearn.experimental.enable_halving_search_cv", "sklearn.experimental.enable_iterative_imputer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_extraction</span></code>.DictVectorizer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_extraction</span></code>.FeatureHasher", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_extraction.image</span></code>.PatchExtractor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_extraction.image</span></code>.extract_patches_2d", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_extraction.image</span></code>.grid_to_graph", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_extraction.image</span></code>.img_to_graph", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_extraction.image</span></code>.reconstruct_from_patches_2d", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_extraction.text</span></code>.CountVectorizer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_extraction.text</span></code>.HashingVectorizer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_extraction.text</span></code>.TfidfTransformer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_extraction.text</span></code>.TfidfVectorizer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_selection</span></code>.GenericUnivariateSelect", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_selection</span></code>.RFE", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_selection</span></code>.RFECV", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_selection</span></code>.SelectFdr", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_selection</span></code>.SelectFpr", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_selection</span></code>.SelectFromModel", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_selection</span></code>.SelectFwe", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_selection</span></code>.SelectKBest", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_selection</span></code>.SelectPercentile", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_selection</span></code>.SelectorMixin", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_selection</span></code>.SequentialFeatureSelector", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_selection</span></code>.VarianceThreshold", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_selection</span></code>.chi2", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_selection</span></code>.f_classif", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_selection</span></code>.f_regression", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_selection</span></code>.mutual_info_classif", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_selection</span></code>.mutual_info_regression", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.feature_selection</span></code>.r_regression", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.gaussian_process</span></code>.GaussianProcessClassifier", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.gaussian_process</span></code>.GaussianProcessRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.gaussian_process.kernels</span></code>.CompoundKernel", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.gaussian_process.kernels</span></code>.ConstantKernel", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.gaussian_process.kernels</span></code>.DotProduct", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.gaussian_process.kernels</span></code>.ExpSineSquared", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.gaussian_process.kernels</span></code>.Exponentiation", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.gaussian_process.kernels</span></code>.Hyperparameter", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.gaussian_process.kernels</span></code>.Kernel", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.gaussian_process.kernels</span></code>.Matern", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.gaussian_process.kernels</span></code>.PairwiseKernel", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.gaussian_process.kernels</span></code>.Product", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.gaussian_process.kernels</span></code>.RBF", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.gaussian_process.kernels</span></code>.RationalQuadratic", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.gaussian_process.kernels</span></code>.Sum", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.gaussian_process.kernels</span></code>.WhiteKernel", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn</span></code>.get_config", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.impute</span></code>.IterativeImputer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.impute</span></code>.KNNImputer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.impute</span></code>.MissingIndicator", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.impute</span></code>.SimpleImputer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.inspection</span></code>.DecisionBoundaryDisplay", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.inspection</span></code>.PartialDependenceDisplay", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.inspection</span></code>.partial_dependence", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.inspection</span></code>.permutation_importance", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.isotonic</span></code>.IsotonicRegression", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.isotonic</span></code>.check_increasing", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.isotonic</span></code>.isotonic_regression", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.kernel_approximation</span></code>.AdditiveChi2Sampler", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.kernel_approximation</span></code>.Nystroem", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.kernel_approximation</span></code>.PolynomialCountSketch", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.kernel_approximation</span></code>.RBFSampler", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.kernel_approximation</span></code>.SkewedChi2Sampler", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.kernel_ridge</span></code>.KernelRidge", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.ARDRegression", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.BayesianRidge", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.ElasticNet", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.ElasticNetCV", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.GammaRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.HuberRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.Lars", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.LarsCV", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.Lasso", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.LassoCV", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.LassoLars", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.LassoLarsCV", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.LassoLarsIC", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.LinearRegression", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.LogisticRegression", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.LogisticRegressionCV", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.MultiTaskElasticNet", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.MultiTaskElasticNetCV", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.MultiTaskLasso", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.MultiTaskLassoCV", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.OrthogonalMatchingPursuit", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.OrthogonalMatchingPursuitCV", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.PassiveAggressiveClassifier", "sklearn.linear_model.PassiveAggressiveRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.Perceptron", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.PoissonRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.QuantileRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.RANSACRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.Ridge", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.RidgeCV", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.RidgeClassifier", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.RidgeClassifierCV", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.SGDClassifier", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.SGDOneClassSVM", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.SGDRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.TheilSenRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.TweedieRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.enet_path", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.lars_path", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.lars_path_gram", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.lasso_path", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.orthogonal_mp", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.orthogonal_mp_gram", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.linear_model</span></code>.ridge_regression", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.manifold</span></code>.Isomap", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.manifold</span></code>.LocallyLinearEmbedding", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.manifold</span></code>.MDS", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.manifold</span></code>.SpectralEmbedding", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.manifold</span></code>.TSNE", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.manifold</span></code>.locally_linear_embedding", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.manifold</span></code>.smacof", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.manifold</span></code>.spectral_embedding", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.manifold</span></code>.trustworthiness", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.ConfusionMatrixDisplay", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.DetCurveDisplay", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.DistanceMetric", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.PrecisionRecallDisplay", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.PredictionErrorDisplay", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.RocCurveDisplay", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.accuracy_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.adjusted_mutual_info_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.adjusted_rand_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.auc", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.average_precision_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.balanced_accuracy_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.brier_score_loss", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.calinski_harabasz_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.check_scoring", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.class_likelihood_ratios", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.classification_report", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics.cluster</span></code>.contingency_matrix", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics.cluster</span></code>.pair_confusion_matrix", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.cohen_kappa_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.completeness_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.confusion_matrix", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.consensus_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.coverage_error", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.d2_absolute_error_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.d2_pinball_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.d2_tweedie_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.davies_bouldin_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.dcg_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.det_curve", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.explained_variance_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.f1_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.fbeta_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.fowlkes_mallows_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.get_scorer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.get_scorer_names", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.hamming_loss", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.hinge_loss", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.homogeneity_completeness_v_measure", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.homogeneity_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.jaccard_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.label_ranking_average_precision_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.label_ranking_loss", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.log_loss", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.make_scorer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.matthews_corrcoef", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.max_error", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.mean_absolute_error", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.mean_absolute_percentage_error", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.mean_gamma_deviance", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.mean_pinball_loss", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.mean_poisson_deviance", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.mean_squared_error", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.mean_squared_log_error", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.mean_tweedie_deviance", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.median_absolute_error", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.multilabel_confusion_matrix", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.mutual_info_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.ndcg_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.normalized_mutual_info_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.additive_chi2_kernel", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.chi2_kernel", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.cosine_distances", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.cosine_similarity", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.distance_metrics", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.euclidean_distances", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.haversine_distances", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.kernel_metrics", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.laplacian_kernel", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.linear_kernel", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.manhattan_distances", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.nan_euclidean_distances", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.paired_cosine_distances", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.paired_distances", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.paired_euclidean_distances", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.paired_manhattan_distances", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.pairwise_kernels", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.polynomial_kernel", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.rbf_kernel", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.sigmoid_kernel", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.pairwise_distances", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.pairwise_distances_argmin", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.pairwise_distances_argmin_min", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.pairwise_distances_chunked", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.precision_recall_curve", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.precision_recall_fscore_support", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.precision_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.r2_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.rand_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.recall_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.roc_auc_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.roc_curve", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.root_mean_squared_error", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.root_mean_squared_log_error", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.silhouette_samples", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.silhouette_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.top_k_accuracy_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.v_measure_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.metrics</span></code>.zero_one_loss", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.mixture</span></code>.BayesianGaussianMixture", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.mixture</span></code>.GaussianMixture", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.GridSearchCV", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.GroupKFold", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.GroupShuffleSplit", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.HalvingGridSearchCV", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.HalvingRandomSearchCV", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.KFold", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.LearningCurveDisplay", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.LeaveOneGroupOut", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.LeaveOneOut", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.LeavePGroupsOut", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.LeavePOut", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.ParameterGrid", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.ParameterSampler", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.PredefinedSplit", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.RandomizedSearchCV", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.RepeatedKFold", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.RepeatedStratifiedKFold", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.ShuffleSplit", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.StratifiedGroupKFold", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.StratifiedKFold", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.StratifiedShuffleSplit", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.TimeSeriesSplit", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.ValidationCurveDisplay", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.check_cv", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.cross_val_predict", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.cross_val_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.cross_validate", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.learning_curve", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.permutation_test_score", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.train_test_split", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.model_selection</span></code>.validation_curve", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.multiclass</span></code>.OneVsOneClassifier", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.multiclass</span></code>.OneVsRestClassifier", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.multiclass</span></code>.OutputCodeClassifier", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.multioutput</span></code>.ClassifierChain", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.multioutput</span></code>.MultiOutputClassifier", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.multioutput</span></code>.MultiOutputRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.multioutput</span></code>.RegressorChain", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.naive_bayes</span></code>.BernoulliNB", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.naive_bayes</span></code>.CategoricalNB", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.naive_bayes</span></code>.ComplementNB", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.naive_bayes</span></code>.GaussianNB", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.naive_bayes</span></code>.MultinomialNB", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.neighbors</span></code>.BallTree", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.neighbors</span></code>.KDTree", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.neighbors</span></code>.KNeighborsClassifier", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.neighbors</span></code>.KNeighborsRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.neighbors</span></code>.KNeighborsTransformer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.neighbors</span></code>.KernelDensity", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.neighbors</span></code>.LocalOutlierFactor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.neighbors</span></code>.NearestCentroid", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.neighbors</span></code>.NearestNeighbors", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.neighbors</span></code>.NeighborhoodComponentsAnalysis", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.neighbors</span></code>.RadiusNeighborsClassifier", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.neighbors</span></code>.RadiusNeighborsRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.neighbors</span></code>.RadiusNeighborsTransformer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.neighbors</span></code>.kneighbors_graph", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.neighbors</span></code>.radius_neighbors_graph", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.neighbors</span></code>.sort_graph_by_row_values", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.neural_network</span></code>.BernoulliRBM", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.neural_network</span></code>.MLPClassifier", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.neural_network</span></code>.MLPRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.pipeline</span></code>.FeatureUnion", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.pipeline</span></code>.Pipeline", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.pipeline</span></code>.make_pipeline", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.pipeline</span></code>.make_union", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.Binarizer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.FunctionTransformer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.KBinsDiscretizer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.KernelCenterer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.LabelBinarizer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.LabelEncoder", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.MaxAbsScaler", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.MinMaxScaler", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.MultiLabelBinarizer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.Normalizer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.OneHotEncoder", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.OrdinalEncoder", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.PolynomialFeatures", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.PowerTransformer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.QuantileTransformer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.RobustScaler", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.SplineTransformer", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.StandardScaler", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.TargetEncoder", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.add_dummy_feature", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.binarize", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.label_binarize", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.maxabs_scale", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.minmax_scale", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.normalize", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.power_transform", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.quantile_transform", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.robust_scale", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.preprocessing</span></code>.scale", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.random_projection</span></code>.GaussianRandomProjection", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.random_projection</span></code>.SparseRandomProjection", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.random_projection</span></code>.johnson_lindenstrauss_min_dim", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.semi_supervised</span></code>.LabelPropagation", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.semi_supervised</span></code>.LabelSpreading", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.semi_supervised</span></code>.SelfTrainingClassifier", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn</span></code>.set_config", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn</span></code>.show_versions", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.svm</span></code>.LinearSVC", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.svm</span></code>.LinearSVR", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.svm</span></code>.NuSVC", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.svm</span></code>.NuSVR", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.svm</span></code>.OneClassSVM", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.svm</span></code>.SVC", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.svm</span></code>.SVR", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.svm</span></code>.l1_min_c", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.tree</span></code>.DecisionTreeClassifier", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.tree</span></code>.DecisionTreeRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.tree</span></code>.ExtraTreeClassifier", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.tree</span></code>.ExtraTreeRegressor", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.tree</span></code>.export_graphviz", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.tree</span></code>.export_text", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.tree</span></code>.plot_tree", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils</span></code>.Bunch", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils</span></code>._safe_indexing", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.arrayfuncs</span></code>.min_pos", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils</span></code>.as_float_array", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils</span></code>.assert_all_finite", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils</span></code>.check_X_y", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils</span></code>.check_array", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils</span></code>.check_consistent_length", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils</span></code>.check_random_state", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils</span></code>.check_scalar", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.class_weight</span></code>.compute_class_weight", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.class_weight</span></code>.compute_sample_weight", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils</span></code>.deprecated", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.discovery</span></code>.all_displays", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.discovery</span></code>.all_estimators", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.discovery</span></code>.all_functions", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.estimator_checks</span></code>.check_estimator", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.estimator_checks</span></code>.parametrize_with_checks", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils</span></code>.estimator_html_repr", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.extmath</span></code>.density", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.extmath</span></code>.fast_logdet", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.extmath</span></code>.randomized_range_finder", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.extmath</span></code>.randomized_svd", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.extmath</span></code>.safe_sparse_dot", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.extmath</span></code>.weighted_mode", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils</span></code>.gen_batches", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils</span></code>.gen_even_slices", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.graph</span></code>.single_source_shortest_path_length", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils</span></code>.indexable", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.metadata_routing</span></code>.MetadataRequest", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.metadata_routing</span></code>.MetadataRouter", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.metadata_routing</span></code>.MethodMapping", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.metadata_routing</span></code>.get_routing_for_object", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.metadata_routing</span></code>.process_routing", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.metaestimators</span></code>.available_if", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.multiclass</span></code>.is_multilabel", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.multiclass</span></code>.type_of_target", "<code class=\"xref py py-mod docutils literal notranslate\"><span 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class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils</span></code>.safe_mask", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils</span></code>.safe_sqr", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils</span></code>.shuffle", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.sparsefuncs</span></code>.incr_mean_variance_axis", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.sparsefuncs</span></code>.inplace_column_scale", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.sparsefuncs</span></code>.inplace_csr_column_scale", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.sparsefuncs</span></code>.inplace_row_scale", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.sparsefuncs</span></code>.inplace_swap_column", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.sparsefuncs</span></code>.inplace_swap_row", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.sparsefuncs</span></code>.mean_variance_axis", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.sparsefuncs_fast</span></code>.inplace_csr_row_normalize_l1", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.sparsefuncs_fast</span></code>.inplace_csr_row_normalize_l2", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.validation</span></code>.check_is_fitted", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.validation</span></code>.check_memory", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.validation</span></code>.check_symmetric", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.validation</span></code>.column_or_1d", "<code class=\"xref py py-mod docutils literal notranslate\"><span class=\"pre\">sklearn.utils.validation</span></code>.has_fit_parameter", "<span class=\"section-number\">3.2. </span>Tuning the hyper-parameters of an estimator", "<span class=\"section-number\">6.4. </span>Imputation of missing values", "<span class=\"section-number\">1.15. </span>Isotonic regression", "<span class=\"section-number\">6.7. </span>Kernel Approximation", "<span class=\"section-number\">1.3. </span>Kernel ridge regression", "<span class=\"section-number\">1.2. </span>Linear and Quadratic Discriminant Analysis", "<span class=\"section-number\">3.4. </span>Validation curves: plotting scores to evaluate models", "<span class=\"section-number\">1.1. </span>Linear Models", "<span class=\"section-number\">2.2. </span>Manifold learning", "<span class=\"section-number\">6.8. </span>Pairwise metrics, Affinities and Kernels", "<span class=\"section-number\">2.1. </span>Gaussian mixture models", "<span class=\"section-number\">3.3. </span>Metrics and scoring: quantifying the quality of predictions", "<span class=\"section-number\">1.12. </span>Multiclass and multioutput algorithms", "<span class=\"section-number\">1.9. </span>Naive Bayes", "<span class=\"section-number\">1.6. </span>Nearest Neighbors", "<span class=\"section-number\">1.17. </span>Neural network models (supervised)", "<span class=\"section-number\">2.9. </span>Neural network models (unsupervised)", "<span class=\"section-number\">2.7. </span>Novelty and Outlier Detection", "<span class=\"section-number\">4.1. </span>Partial Dependence and Individual Conditional Expectation plots", "<span class=\"section-number\">4.2. </span>Permutation feature importance", "&lt;no title&gt;", "<span class=\"section-number\">6.3. </span>Preprocessing data", "<span class=\"section-number\">6.9. </span>Transforming the prediction target (<code class=\"docutils literal notranslate\"><span class=\"pre\">y</span></code>)", "<span class=\"section-number\">6.6. </span>Random Projection", "<span class=\"section-number\">1.14. </span>Semi-supervised learning", "<span class=\"section-number\">1.5. </span>Stochastic Gradient Descent", "<span class=\"section-number\">1.4. </span>Support Vector Machines", "<span class=\"section-number\">1.10. </span>Decision Trees", "<span class=\"section-number\">6.5. </span>Unsupervised dimensionality reduction", "Welcome to scikit-learn", "External Resources, Videos and Talks", "Related Projects", "Roadmap", "Computation times", "<span class=\"section-number\">1. </span>Supervised learning", "Support", "Who is using scikit-learn?", "&lt;no title&gt;", "An introduction to machine learning with scikit-learn", "scikit-learn Tutorials", "Choosing the right estimator", "A tutorial on statistical-learning for scientific data processing", "Model selection: choosing estimators and their parameters", "Putting it all together", "Statistical learning: the setting and the estimator object in scikit-learn", "Supervised learning: predicting an output variable from high-dimensional observations", "Unsupervised learning: seeking representations of the data", "Working With Text Data", "<span class=\"section-number\">2. </span>Unsupervised learning", "User guide: contents", "Available documentation for Scikit-learn", "<span class=\"section-number\">5. </span>Visualizations", "Release History", "&lt;no title&gt;", "Version 0.12.1", "Version 0.13.1", "Version 0.14", "Version 0.15.2", "Version 0.16.1", "Version 0.17.1", "Version 0.18.2", "Version 0.19.2", "Version 0.20.4", "Version 0.21.3", "Version 0.22.2.post1", "Version 0.23.2", "Version 0.24.2", "Version 1.0.2", "Version 1.1.3", "Version 1.2.2", "Version 1.3.2", "Version 1.4.1", "Version 1.5.0"], 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275, 277, 279, 284, 286, 287, 288, 290, 293, 297, 307, 310, 312, 313, 316, 318, 320, 321, 322, 326, 327, 336, 338, 339, 340, 341, 342, 344, 345, 347, 349, 353, 354, 358, 359, 360, 363, 370, 373, 375, 376, 384, 387, 395, 397, 399, 404, 405, 406, 408, 409, 411, 413, 414, 415, 416, 417, 418, 427, 431, 433, 436, 441, 448, 464, 466, 468, 469, 470, 471, 472, 473, 474, 475, 481, 482, 483, 484, 487, 488, 489, 495, 497, 499, 500, 501, 502, 508, 521, 523, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 542, 543, 547, 548, 549, 550, 551, 552, 553, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 569, 587, 588, 590, 592, 593, 596, 601, 605, 608, 609, 610, 614, 626, 631, 632, 633, 634, 636, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 690, 700, 705, 710, 720, 721, 722, 732, 733, 739, 748, 751, 783, 786, 787, 790, 795, 796, 797, 800, 801, 802, 803, 811, 812, 813, 814, 818, 819, 822, 823, 824, 825, 826, 827, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 843, 845, 846, 847, 849, 851, 856, 857, 858, 860, 861, 863, 865, 867, 872, 873, 875, 876, 877, 879, 880, 881, 885, 886, 888, 889, 890, 891, 895, 896, 897, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 915, 978, 980, 981, 982, 985, 986, 989, 991, 994, 995, 996, 998, 999, 1000, 1001, 1002, 1003, 1004, 1008, 1009, 1010, 1011, 1013, 1016, 1018, 1020, 1024, 1025, 1026, 1031, 1032, 1033, 1034, 1035, 1036], "complex": [3, 9, 10, 15, 16, 17, 21, 73, 118, 132, 148, 152, 157, 164, 167, 204, 214, 217, 228, 235, 236, 244, 254, 259, 262, 265, 270, 277, 307, 316, 319, 320, 325, 332, 339, 343, 354, 356, 361, 362, 377, 381, 386, 393, 409, 413, 415, 419, 439, 443, 446, 447, 449, 466, 488, 499, 500, 501, 513, 533, 540, 550, 556, 557, 558, 559, 561, 563, 564, 603, 629, 637, 655, 657, 675, 676, 732, 748, 797, 826, 828, 856, 860, 861, 865, 873, 880, 896, 900, 903, 906, 908, 909, 910, 911, 977, 978, 980, 981, 983, 985, 987, 989, 994, 998, 1001, 1009, 1010, 1011, 1013, 1026, 1032, 1034, 1040, 1041, 1044], "influenc": [3, 9, 13, 16, 81, 82, 112, 167, 170, 182, 235, 246, 269, 306, 313, 339, 343, 347, 363, 369, 410, 413, 415, 431, 439, 453, 464, 481, 482, 483, 488, 500, 553, 555, 557, 559, 561, 564, 567, 569, 610, 627, 634, 642, 643, 644, 645, 646, 648, 649, 650, 651, 652, 653, 654, 655, 656, 659, 660, 661, 662, 663, 664, 666, 669, 671, 672, 675, 677, 678, 732, 748, 826, 833, 834, 842, 843, 850, 851, 858, 877, 878, 889, 901, 903, 906, 909, 911, 920, 977, 982, 983, 984, 991, 994, 998, 1002, 1003, 1010, 1013, 1034, 1039, 1040], "core": [3, 10, 16, 50, 56, 66, 72, 112, 118, 120, 121, 167, 170, 171, 321, 366, 368, 370, 379, 381, 384, 389, 395, 398, 408, 413, 415, 416, 419, 443, 445, 448, 449, 454, 456, 498, 537, 588, 593, 657, 658, 665, 667, 675, 835, 836, 837, 838, 839, 844, 852, 984, 991, 994, 1003, 1006, 1009, 1010, 1011, 1013, 1022, 1024, 1026, 1033, 1035, 1036, 1038, 1041, 1045, 1046, 1047], "classif": [3, 6, 7, 10, 15, 16, 17, 20, 24, 25, 26, 28, 29, 32, 35, 36, 46, 72, 73, 74, 79, 80, 86, 89, 93, 94, 99, 112, 114, 116, 118, 119, 120, 121, 126, 127, 128, 130, 133, 134, 137, 141, 142, 144, 145, 146, 147, 148, 149, 151, 157, 158, 159, 162, 170, 171, 176, 178, 190, 191, 192, 193, 200, 208, 210, 211, 212, 213, 215, 219, 228, 230, 234, 235, 239, 242, 244, 249, 250, 254, 255, 258, 259, 260, 261, 262, 270, 275, 276, 277, 279, 281, 283, 285, 286, 292, 293, 294, 296, 297, 298, 299, 300, 302, 304, 308, 309, 310, 311, 326, 331, 332, 334, 335, 339, 342, 344, 345, 346, 347, 348, 350, 351, 353, 354, 355, 363, 367, 369, 370, 374, 375, 376, 378, 383, 386, 390, 394, 395, 403, 406, 408, 409, 412, 413, 416, 417, 425, 436, 468, 474, 481, 486, 487, 488, 490, 491, 492, 493, 494, 495, 496, 498, 499, 501, 502, 503, 509, 511, 513, 514, 518, 519, 521, 522, 540, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 563, 564, 565, 566, 568, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 603, 604, 605, 608, 609, 610, 612, 613, 621, 630, 631, 632, 642, 657, 658, 665, 667, 673, 674, 675, 696, 702, 704, 707, 711, 712, 714, 715, 717, 724, 725, 727, 728, 732, 733, 736, 739, 740, 741, 752, 754, 756, 757, 759, 780, 781, 782, 784, 785, 786, 787, 792, 794, 797, 798, 800, 801, 802, 803, 811, 815, 816, 819, 820, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 834, 835, 836, 837, 838, 839, 842, 847, 849, 850, 856, 857, 858, 860, 864, 867, 872, 873, 874, 875, 880, 881, 884, 886, 895, 896, 897, 900, 902, 903, 904, 905, 906, 908, 909, 910, 911, 912, 913, 914, 923, 926, 934, 977, 978, 980, 981, 982, 985, 986, 990, 993, 995, 996, 998, 999, 1001, 1007, 1008, 1010, 1011, 1012, 1013, 1015, 1016, 1020, 1021, 1026, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1043, 1044, 1045, 1046, 1047, 1048], "text": [3, 6, 7, 10, 11, 12, 15, 16, 17, 18, 20, 32, 33, 38, 41, 45, 54, 58, 59, 61, 62, 63, 65, 71, 81, 82, 118, 123, 173, 180, 194, 195, 229, 233, 240, 241, 246, 248, 254, 255, 260, 265, 267, 268, 272, 277, 289, 292, 294, 300, 308, 319, 325, 327, 332, 346, 355, 357, 367, 369, 370, 373, 374, 375, 381, 386, 393, 395, 406, 408, 409, 411, 412, 413, 415, 418, 446, 448, 467, 486, 487, 488, 498, 502, 507, 520, 535, 543, 563, 614, 624, 651, 657, 665, 667, 673, 675, 696, 702, 704, 706, 712, 716, 727, 735, 791, 793, 797, 811, 826, 835, 837, 839, 842, 847, 860, 861, 863, 864, 872, 896, 897, 898, 900, 912, 913, 914, 934, 977, 980, 984, 986, 988, 989, 990, 992, 995, 998, 1000, 1002, 1003, 1007, 1009, 1010, 1013, 1016, 1026, 1031, 1032, 1033, 1034, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048], "document": [3, 6, 7, 10, 12, 15, 16, 18, 19, 22, 23, 27, 32, 38, 46, 52, 58, 59, 61, 62, 65, 71, 72, 73, 74, 90, 91, 93, 96, 117, 119, 120, 128, 129, 132, 135, 146, 157, 170, 171, 172, 181, 183, 197, 198, 215, 230, 231, 232, 233, 236, 237, 239, 240, 241, 242, 243, 251, 255, 259, 261, 262, 268, 273, 302, 303, 312, 317, 320, 321, 323, 328, 330, 344, 347, 354, 355, 361, 367, 368, 369, 370, 375, 376, 377, 379, 380, 382, 383, 384, 385, 386, 387, 389, 393, 395, 398, 399, 402, 405, 407, 408, 413, 416, 419, 443, 446, 447, 448, 449, 452, 456, 463, 487, 488, 498, 500, 510, 520, 522, 535, 543, 563, 587, 588, 589, 590, 593, 603, 630, 638, 642, 657, 658, 665, 667, 672, 673, 674, 675, 695, 696, 698, 702, 704, 710, 716, 735, 756, 757, 767, 776, 777, 778, 791, 793, 797, 800, 801, 803, 811, 819, 822, 824, 827, 830, 837, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 850, 851, 852, 853, 854, 861, 872, 900, 905, 916, 919, 921, 934, 954, 977, 984, 986, 988, 989, 990, 991, 992, 996, 1002, 1005, 1006, 1007, 1010, 1013, 1017, 1022, 1024, 1032, 1033, 1037], "outlier": [3, 16, 35, 45, 55, 81, 82, 83, 125, 128, 152, 167, 178, 180, 198, 202, 203, 204, 205, 206, 214, 219, 228, 244, 285, 298, 305, 310, 313, 314, 338, 349, 376, 395, 408, 410, 415, 430, 445, 463, 465, 468, 469, 472, 473, 474, 475, 489, 490, 491, 495, 509, 511, 521, 523, 562, 630, 638, 648, 669, 670, 671, 675, 676, 677, 678, 691, 701, 826, 846, 850, 861, 869, 870, 872, 873, 874, 876, 877, 878, 879, 880, 885, 886, 887, 888, 889, 890, 891, 903, 904, 906, 988, 1002, 1003, 1008, 1010, 1011, 1025, 1026, 1031, 1036, 1037, 1039], "detect": [3, 8, 10, 16, 21, 35, 64, 66, 81, 82, 83, 119, 122, 128, 167, 181, 204, 214, 217, 228, 242, 244, 254, 261, 264, 270, 271, 277, 285, 298, 306, 312, 338, 376, 379, 381, 385, 393, 395, 406, 407, 408, 410, 412, 416, 430, 445, 448, 463, 465, 468, 469, 472, 473, 474, 475, 487, 488, 490, 491, 495, 509, 511, 514, 521, 536, 537, 538, 545, 562, 563, 587, 590, 595, 630, 638, 676, 697, 701, 725, 787, 826, 846, 850, 861, 870, 873, 874, 878, 879, 880, 900, 904, 980, 984, 1008, 1009, 1010, 1011, 1013, 1019, 1024, 1025, 1026, 1031, 1034, 1037, 1038, 1039, 1041, 1043, 1044, 1046, 1047], "set": [3, 4, 5, 6, 7, 8, 11, 13, 14, 16, 17, 21, 26, 27, 28, 31, 32, 35, 38, 42, 43, 45, 46, 47, 50, 51, 54, 55, 56, 58, 59, 61, 63, 65, 66, 67, 71, 72, 74, 81, 82, 83, 87, 91, 95, 96, 100, 101, 104, 107, 110, 112, 115, 116, 117, 118, 119, 120, 121, 122, 123, 125, 126, 127, 128, 134, 138, 145, 146, 149, 152, 158, 159, 160, 161, 167, 169, 170, 171, 172, 173, 179, 184, 185, 188, 189, 191, 193, 195, 196, 197, 199, 200, 201, 202, 205, 208, 213, 214, 218, 221, 222, 223, 229, 230, 231, 232, 234, 235, 236, 239, 243, 246, 248, 250, 251, 255, 256, 259, 261, 262, 263, 264, 265, 266, 269, 270, 271, 272, 275, 279, 282, 286, 290, 291, 292, 293, 294, 297, 301, 302, 303, 306, 308, 310, 311, 312, 313, 316, 318, 319, 323, 326, 327, 330, 331, 336, 338, 341, 343, 347, 352, 353, 354, 359, 360, 363, 367, 368, 369, 370, 373, 374, 376, 378, 379, 381, 383, 385, 386, 388, 389, 394, 395, 396, 398, 399, 404, 405, 406, 408, 409, 410, 411, 413, 414, 415, 416, 417, 418, 419, 420, 422, 425, 432, 433, 436, 437, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 456, 457, 458, 459, 460, 462, 463, 464, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 477, 481, 482, 483, 484, 487, 488, 492, 494, 495, 496, 498, 501, 502, 503, 507, 508, 509, 514, 522, 523, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 604, 605, 606, 607, 609, 610, 611, 612, 613, 614, 615, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 693, 696, 697, 698, 699, 700, 701, 702, 712, 713, 718, 720, 721, 724, 725, 726, 727, 728, 729, 732, 736, 738, 739, 743, 744, 746, 748, 749, 751, 754, 777, 778, 780, 781, 782, 783, 785, 787, 788, 789, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 856, 857, 858, 859, 860, 861, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 884, 885, 886, 888, 889, 890, 891, 892, 893, 895, 896, 897, 898, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 914, 915, 920, 921, 931, 932, 937, 941, 944, 945, 956, 957, 958, 959, 962, 963, 969, 972, 977, 978, 979, 980, 981, 982, 983, 985, 986, 987, 988, 989, 990, 991, 992, 994, 995, 996, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1008, 1009, 1010, 1016, 1018, 1019, 1020, 1023, 1031, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048], "predict": [3, 6, 7, 8, 11, 16, 24, 26, 27, 28, 31, 33, 35, 38, 45, 49, 57, 58, 59, 63, 71, 72, 73, 74, 76, 86, 106, 110, 111, 112, 114, 115, 116, 118, 120, 121, 122, 123, 126, 128, 129, 130, 131, 132, 133, 134, 136, 140, 141, 146, 149, 151, 152, 154, 155, 157, 158, 159, 160, 161, 162, 164, 165, 167, 170, 171, 172, 173, 176, 179, 182, 183, 184, 192, 195, 196, 197, 198, 201, 202, 203, 204, 206, 207, 208, 209, 214, 215, 217, 218, 229, 230, 232, 234, 236, 238, 247, 248, 249, 250, 251, 252, 254, 255, 259, 261, 263, 264, 267, 270, 271, 275, 277, 279, 282, 287, 290, 291, 292, 295, 296, 303, 306, 307, 311, 312, 316, 317, 318, 319, 320, 322, 323, 327, 329, 330, 331, 335, 336, 338, 339, 343, 345, 346, 347, 348, 352, 353, 358, 359, 360, 361, 363, 366, 368, 370, 373, 376, 378, 381, 383, 388, 390, 395, 397, 399, 402, 403, 404, 406, 407, 408, 409, 413, 415, 416, 417, 422, 425, 431, 436, 437, 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1015, 1016, 1019, 1020, 1021, 1022, 1023, 1027, 1028, 1029, 1031, 1032, 1033, 1035, 1036, 1048], "as_fram": [4, 5, 13, 72, 76, 118, 122, 132, 157, 170, 171, 172, 173, 188, 189, 200, 208, 216, 218, 239, 243, 279, 284, 287, 302, 311, 312, 316, 318, 320, 321, 375, 376, 488, 489, 490, 491, 495, 499, 500, 501, 503, 504, 509, 1041, 1042, 1043, 1044, 1047], "true": [4, 5, 6, 7, 8, 10, 11, 13, 15, 17, 18, 20, 27, 31, 33, 35, 38, 45, 50, 51, 54, 55, 56, 58, 59, 61, 64, 71, 72, 73, 74, 76, 79, 80, 81, 82, 83, 86, 93, 96, 97, 98, 99, 101, 106, 110, 112, 116, 118, 122, 124, 125, 127, 128, 129, 132, 133, 135, 138, 139, 140, 145, 148, 152, 156, 157, 158, 159, 160, 161, 164, 165, 169, 170, 171, 172, 173, 176, 184, 185, 187, 188, 189, 191, 193, 195, 196, 197, 200, 202, 203, 204, 207, 208, 214, 215, 216, 218, 221, 224, 225, 229, 230, 233, 235, 236, 237, 238, 239, 242, 243, 246, 249, 251, 255, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 279, 282, 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597, 598, 599, 600, 601, 602, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 617, 618, 619, 620, 621, 622, 623, 624, 626, 627, 628, 629, 630, 631, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 693, 694, 696, 697, 698, 699, 700, 701, 702, 704, 706, 707, 708, 711, 712, 713, 714, 717, 719, 722, 724, 725, 726, 727, 728, 729, 731, 732, 733, 736, 737, 738, 739, 740, 741, 748, 749, 750, 752, 754, 759, 765, 767, 776, 780, 781, 782, 783, 784, 785, 786, 787, 792, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 806, 808, 809, 811, 815, 816, 820, 821, 822, 823, 824, 825, 826, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 895, 896, 897, 898, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 918, 919, 920, 921, 931, 937, 938, 949, 950, 953, 954, 958, 959, 961, 969, 974, 976, 977, 978, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 998, 1000, 1001, 1002, 1003, 1004, 1008, 1015, 1019, 1020, 1022, 1023, 1024, 1028, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048], "df": [4, 13, 26, 28, 132, 169, 171, 179, 184, 200, 218, 251, 261, 310, 312, 323, 353, 386, 416, 589, 590, 977, 978, 1048], "frame": [4, 13, 17, 72, 157, 170, 171, 200, 312, 323, 376, 381, 393, 396, 488, 489, 490, 491, 495, 499, 500, 501, 503, 504, 509, 1008, 1020, 1043], "To": [4, 8, 17, 27, 28, 33, 47, 56, 58, 79, 81, 83, 87, 101, 112, 122, 125, 126, 134, 138, 149, 157, 158, 161, 164, 165, 169, 170, 176, 181, 184, 188, 200, 201, 202, 204, 208, 218, 233, 234, 236, 239, 241, 243, 251, 256, 258, 259, 261, 263, 264, 266, 268, 279, 282, 302, 306, 311, 312, 313, 316, 317, 318, 319, 320, 321, 322, 323, 349, 353, 357, 363, 367, 368, 374, 375, 379, 381, 382, 383, 385, 386, 387, 389, 393, 395, 396, 399, 402, 404, 405, 408, 409, 410, 411, 412, 413, 415, 416, 445, 447, 448, 449, 460, 463, 466, 491, 495, 501, 502, 506, 507, 530, 536, 537, 538, 540, 542, 543, 544, 545, 556, 557, 558, 559, 563, 564, 568, 596, 610, 626, 631, 645, 646, 651, 652, 655, 657, 658, 659, 660, 661, 662, 683, 699, 711, 726, 761, 783, 791, 800, 801, 822, 829, 860, 871, 876, 890, 891, 900, 901, 908, 909, 910, 911, 927, 937, 956, 975, 977, 978, 980, 983, 984, 985, 986, 988, 989, 990, 991, 994, 995, 998, 1002, 1003, 1004, 1012, 1013, 1015, 1019, 1021, 1022, 1024, 1031, 1034, 1036, 1037, 1038, 1040, 1042], "get": [4, 5, 9, 12, 17, 18, 20, 38, 49, 53, 57, 97, 101, 125, 134, 146, 149, 152, 157, 158, 164, 170, 171, 172, 173, 193, 202, 215, 218, 221, 237, 259, 262, 263, 265, 269, 272, 279, 284, 289, 297, 307, 316, 323, 327, 330, 336, 339, 341, 344, 349, 352, 354, 357, 368, 370, 374, 375, 376, 379, 381, 382, 383, 386, 387, 388, 389, 395, 398, 402, 404, 406, 407, 408, 409, 410, 412, 413, 415, 416, 417, 418, 422, 423, 424, 429, 431, 436, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 461, 463, 464, 468, 469, 470, 471, 472, 473, 474, 475, 481, 482, 483, 484, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 580, 581, 582, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 606, 607, 609, 610, 611, 612, 613, 614, 615, 617, 618, 619, 620, 621, 622, 623, 624, 626, 627, 628, 629, 633, 634, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 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530, 536, 541, 544, 545, 547, 548, 549, 557, 559, 560, 561, 564, 587, 589, 590, 592, 596, 601, 618, 626, 631, 645, 648, 649, 651, 653, 655, 670, 671, 673, 675, 677, 681, 682, 703, 716, 720, 734, 735, 743, 744, 746, 751, 753, 755, 793, 799, 803, 814, 817, 824, 826, 838, 840, 841, 845, 864, 869, 872, 885, 909, 911, 974, 984, 985, 998, 1002, 1004, 1022, 1023, 1032, 1037, 1042, 1043, 1044, 1045, 1048], "basi": [4, 6, 14, 101, 152, 157, 159, 201, 302, 319, 339, 343, 373, 379, 382, 408, 413, 415, 451, 533, 621, 631, 638, 690, 879, 986, 998, 1003, 1011, 1019, 1020, 1023, 1026, 1039, 1041, 1044], "max": [4, 8, 10, 11, 12, 13, 18, 20, 26, 28, 32, 34, 41, 45, 53, 59, 63, 68, 73, 78, 80, 83, 84, 96, 99, 103, 106, 112, 114, 121, 127, 130, 138, 140, 141, 144, 145, 149, 154, 156, 157, 165, 167, 170, 182, 187, 188, 189, 193, 195, 202, 203, 206, 214, 216, 218, 224, 233, 237, 259, 260, 261, 272, 274, 282, 284, 286, 290, 291, 295, 297, 300, 302, 306, 308, 309, 311, 319, 329, 331, 345, 348, 367, 408, 410, 413, 468, 469, 470, 471, 472, 473, 474, 475, 476, 511, 535, 554, 555, 556, 557, 558, 559, 562, 563, 564, 605, 626, 630, 631, 647, 648, 668, 671, 673, 679, 690, 691, 695, 698, 703, 704, 739, 755, 790, 791, 797, 857, 858, 869, 870, 872, 879, 886, 887, 908, 909, 910, 911, 956, 984, 986, 991, 998, 1002, 1003, 1010, 1022, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047], "977": [4, 13, 412], "rescal": [4, 47, 48, 56, 101, 181, 188, 224, 239, 306, 349, 410, 420, 468, 469, 470, 471, 472, 473, 474, 475, 532, 537, 645, 648, 651, 872, 878, 902, 903, 904, 905, 906, 988, 998, 1023, 1035, 1036], "variabl": [4, 9, 11, 12, 13, 17, 38, 71, 115, 119, 122, 125, 126, 140, 141, 144, 149, 159, 160, 164, 165, 171, 172, 176, 184, 189, 200, 204, 205, 218, 223, 239, 252, 261, 264, 271, 279, 282, 311, 312, 313, 363, 367, 369, 376, 378, 379, 381, 383, 385, 386, 389, 395, 404, 408, 409, 410, 411, 413, 415, 416, 417, 449, 481, 482, 483, 486, 498, 500, 507, 514, 531, 533, 535, 540, 543, 552, 558, 559, 603, 605, 606, 607, 639, 649, 650, 653, 654, 660, 681, 682, 694, 708, 745, 798, 799, 802, 804, 805, 806, 807, 812, 813, 814, 815, 816, 817, 820, 821, 822, 823, 825, 828, 832, 833, 863, 874, 877, 881, 889, 901, 977, 978, 980, 984, 987, 988, 989, 990, 991, 993, 995, 996, 998, 1002, 1004, 1008, 1009, 1012, 1015, 1016, 1018, 1020, 1031, 1036, 1039, 1041, 1044, 1046], "rel": [4, 6, 8, 18, 21, 28, 56, 62, 101, 119, 157, 173, 176, 180, 200, 218, 249, 290, 291, 307, 317, 320, 339, 343, 352, 353, 376, 380, 383, 389, 395, 406, 408, 412, 415, 416, 418, 439, 442, 446, 448, 458, 520, 523, 530, 533, 536, 540, 556, 557, 558, 559, 563, 564, 565, 603, 648, 657, 658, 670, 671, 673, 689, 693, 727, 745, 797, 800, 801, 803, 811, 819, 824, 827, 840, 841, 845, 857, 858, 869, 870, 880, 896, 900, 901, 902, 905, 907, 908, 909, 910, 911, 984, 985, 988, 990, 991, 992, 994, 1012, 1020], "so": [4, 9, 12, 13, 17, 18, 43, 49, 56, 57, 62, 66, 82, 118, 121, 122, 123, 124, 131, 152, 157, 170, 172, 176, 180, 200, 201, 229, 236, 239, 261, 279, 284, 290, 291, 294, 301, 302, 307, 312, 313, 317, 329, 339, 352, 354, 361, 363, 367, 368, 369, 375, 376, 380, 381, 383, 385, 386, 387, 389, 390, 395, 399, 405, 406, 407, 408, 409, 410, 411, 412, 413, 415, 416, 417, 418, 421, 422, 436, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 455, 464, 468, 469, 470, 471, 472, 473, 474, 475, 481, 482, 483, 484, 495, 507, 508, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 580, 581, 582, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 601, 602, 603, 606, 607, 609, 610, 611, 612, 613, 614, 615, 617, 618, 619, 620, 621, 622, 623, 624, 626, 627, 628, 629, 634, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 687, 688, 689, 690, 691, 694, 704, 707, 715, 724, 739, 754, 786, 795, 796, 797, 800, 801, 803, 804, 811, 819, 821, 822, 823, 824, 825, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 856, 857, 858, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 884, 890, 892, 893, 895, 896, 897, 900, 901, 902, 903, 904, 905, 906, 908, 909, 910, 911, 937, 954, 963, 977, 978, 980, 982, 983, 984, 985, 987, 988, 989, 990, 991, 992, 994, 998, 1000, 1001, 1002, 1003, 1008, 1009, 1013, 1022, 1023, 1024, 1031, 1033, 1034, 1037, 1038, 1039, 1040, 1041, 1042, 1044, 1045, 1047, 1048], "error": [4, 5, 7, 13, 14, 18, 28, 55, 76, 79, 80, 82, 100, 105, 111, 115, 118, 119, 122, 124, 126, 136, 149, 152, 164, 167, 170, 184, 189, 196, 199, 200, 202, 203, 206, 207, 214, 218, 232, 235, 236, 242, 252, 254, 257, 261, 262, 266, 270, 271, 275, 277, 279, 290, 291, 320, 323, 338, 347, 352, 353, 368, 378, 379, 381, 383, 384, 385, 386, 390, 393, 395, 399, 402, 406, 407, 408, 409, 410, 412, 413, 415, 416, 417, 436, 442, 443, 446, 448, 463, 464, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 481, 482, 483, 495, 502, 514, 523, 530, 532, 533, 536, 537, 541, 542, 544, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 574, 580, 581, 587, 589, 590, 591, 593, 594, 595, 597, 598, 599, 605, 609, 610, 628, 631, 634, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 684, 687, 688, 689, 691, 692, 695, 697, 700, 701, 708, 711, 719, 720, 721, 725, 738, 741, 742, 743, 744, 745, 746, 748, 749, 751, 762, 776, 780, 786, 787, 788, 789, 797, 798, 799, 800, 801, 803, 804, 806, 811, 815, 819, 822, 823, 824, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 845, 847, 848, 850, 851, 852, 855, 857, 858, 860, 861, 863, 865, 866, 867, 872, 873, 874, 879, 880, 895, 896, 898, 900, 901, 902, 903, 904, 905, 906, 908, 909, 910, 911, 918, 919, 920, 921, 924, 951, 972, 975, 977, 978, 979, 981, 982, 983, 985, 989, 992, 996, 998, 1002, 1003, 1004, 1010, 1011, 1022, 1026, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048], "easili": [4, 9, 32, 91, 146, 173, 203, 216, 234, 236, 240, 243, 261, 263, 270, 289, 308, 381, 383, 395, 396, 409, 415, 416, 418, 448, 982, 984, 985, 998, 1002, 1003, 1004, 1016, 1019, 1024, 1037], "interpret": [4, 28, 149, 159, 167, 168, 169, 171, 174, 196, 200, 201, 205, 216, 218, 255, 261, 263, 264, 303, 307, 310, 318, 352, 354, 367, 379, 381, 382, 387, 389, 390, 395, 397, 406, 408, 409, 414, 416, 451, 463, 464, 466, 495, 540, 571, 610, 638, 642, 652, 671, 672, 690, 691, 694, 700, 727, 751, 756, 757, 781, 790, 791, 803, 812, 823, 824, 826, 829, 861, 873, 880, 956, 982, 984, 988, 989, 995, 998, 1004, 1008, 1010, 1015, 1023, 1031, 1032, 1036, 1039, 1044, 1045, 1046], "fraction": [4, 26, 28, 125, 200, 208, 218, 347, 353, 406, 412, 415, 437, 438, 448, 449, 455, 456, 514, 526, 556, 557, 558, 559, 563, 564, 565, 589, 592, 601, 665, 666, 667, 670, 675, 676, 677, 702, 720, 721, 722, 732, 792, 794, 799, 803, 824, 825, 839, 867, 902, 903, 904, 908, 909, 910, 911, 954, 984, 988, 991, 995, 996, 998, 1003, 1004, 1009, 1032, 1033, 1034, 1045], "maximum": [4, 8, 11, 17, 28, 34, 38, 58, 79, 80, 81, 82, 83, 92, 98, 112, 114, 117, 118, 121, 153, 155, 159, 167, 178, 188, 219, 239, 271, 286, 290, 291, 297, 306, 334, 335, 337, 338, 341, 343, 350, 353, 357, 359, 360, 367, 386, 387, 394, 395, 405, 406, 408, 410, 411, 413, 415, 416, 418, 419, 420, 439, 440, 441, 442, 443, 444, 446, 447, 448, 449, 453, 456, 458, 460, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 481, 482, 483, 497, 508, 510, 511, 512, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 544, 545, 546, 547, 551, 552, 553, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 582, 583, 596, 609, 626, 630, 631, 634, 636, 643, 644, 645, 646, 648, 649, 650, 651, 652, 653, 654, 655, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 670, 671, 673, 675, 676, 677, 678, 681, 682, 684, 685, 686, 687, 688, 689, 691, 692, 693, 715, 742, 779, 795, 797, 800, 801, 803, 811, 818, 824, 849, 856, 857, 858, 864, 865, 869, 870, 872, 875, 876, 877, 879, 888, 889, 894, 895, 896, 897, 900, 901, 905, 908, 909, 910, 911, 912, 914, 917, 924, 977, 983, 984, 985, 987, 988, 990, 991, 992, 994, 998, 1002, 1003, 1004, 1010, 1023, 1038, 1039, 1041, 1042, 1045, 1046, 1048], "fit": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 20, 22, 25, 26, 28, 31, 32, 33, 34, 35, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 50, 51, 52, 53, 55, 56, 57, 59, 62, 63, 64, 65, 66, 68, 71, 72, 73, 74, 75, 76, 79, 80, 81, 82, 83, 86, 87, 96, 98, 99, 100, 101, 102, 104, 105, 106, 110, 112, 113, 114, 115, 116, 117, 118, 121, 122, 123, 124, 127, 128, 129, 130, 131, 132, 133, 134, 135, 138, 140, 141, 145, 146, 147, 148, 149, 152, 153, 154, 155, 156, 158, 159, 160, 161, 167, 169, 170, 171, 172, 173, 176, 178, 182, 183, 184, 186, 188, 189, 190, 191, 192, 193, 195, 196, 198, 199, 200, 201, 203, 204, 205, 207, 208, 209, 212, 213, 214, 215, 216, 217, 218, 219, 224, 229, 230, 232, 234, 235, 236, 237, 238, 239, 240, 242, 243, 246, 247, 248, 249, 250, 251, 252, 255, 256, 258, 259, 260, 261, 262, 263, 264, 265, 266, 269, 270, 271, 272, 273, 274, 275, 279, 284, 286, 287, 288, 289, 290, 292, 293, 294, 295, 297, 300, 301, 302, 303, 305, 307, 308, 309, 310, 311, 312, 314, 316, 317, 318, 319, 320, 321, 322, 323, 326, 327, 328, 329, 330, 331, 335, 336, 337, 338, 339, 340, 341, 343, 344, 345, 347, 348, 349, 352, 353, 357, 358, 359, 360, 361, 367, 369, 370, 373, 376, 378, 381, 384, 386, 387, 390, 393, 395, 402, 404, 405, 406, 407, 408, 409, 410, 412, 413, 414, 416, 417, 418, 419, 422, 424, 425, 426, 430, 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868, 870, 871, 873, 874, 877, 878, 879, 881, 886, 889, 890, 894, 900, 901, 905, 908, 909, 910, 911, 913, 934, 945, 946, 957, 977, 980, 982, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 998, 999, 1000, 1002, 1003, 1004, 1005, 1009, 1010, 1022, 1023, 1031, 1034, 1035, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048], "side": [4, 17, 28, 60, 125, 193, 204, 218, 223, 232, 306, 343, 352, 363, 376, 393, 406, 409, 412, 413, 416, 425, 431, 514, 984, 985, 988, 990, 1003, 1040], "split": [4, 5, 6, 11, 13, 15, 18, 25, 27, 32, 33, 71, 101, 112, 117, 118, 119, 121, 123, 124, 125, 126, 127, 128, 138, 145, 149, 169, 170, 173, 184, 195, 196, 200, 203, 208, 218, 230, 236, 239, 247, 248, 255, 256, 259, 261, 266, 268, 271, 279, 287, 293, 300, 302, 307, 308, 313, 320, 322, 329, 339, 352, 354, 361, 376, 381, 388, 394, 395, 398, 399, 406, 408, 413, 415, 416, 417, 436, 441, 442, 471, 472, 478, 479, 496, 530, 536, 538, 541, 545, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 587, 588, 590, 593, 601, 646, 650, 652, 654, 660, 662, 664, 672, 674, 694, 703, 704, 716, 729, 735, 755, 793, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 831, 834, 840, 841, 857, 858, 881, 885, 886, 888, 889, 890, 891, 908, 909, 910, 911, 959, 977, 980, 984, 985, 991, 994, 995, 996, 998, 1002, 1004, 1009, 1015, 1019, 1020, 1022, 1023, 1024, 1026, 1031, 1034, 1036, 1037, 1038, 1039, 1040, 1042, 1043, 1044, 1045, 1046, 1047, 1048], "limit": [4, 8, 18, 38, 43, 58, 67, 83, 112, 119, 120, 126, 132, 164, 170, 171, 172, 179, 200, 201, 217, 233, 268, 308, 312, 319, 347, 354, 363, 368, 382, 383, 386, 393, 395, 396, 406, 408, 411, 413, 417, 445, 462, 467, 558, 559, 560, 561, 631, 649, 657, 658, 678, 681, 682, 687, 776, 818, 848, 850, 851, 852, 873, 874, 898, 902, 903, 904, 905, 906, 956, 977, 984, 985, 987, 988, 995, 996, 998, 1009, 1015, 1026, 1039, 1041, 1044, 1045, 1048], "cv": [4, 12, 13, 25, 26, 27, 55, 72, 73, 74, 75, 79, 103, 118, 122, 124, 138, 139, 148, 164, 165, 170, 189, 199, 202, 257, 259, 260, 261, 262, 263, 264, 266, 267, 271, 275, 279, 312, 321, 323, 339, 347, 394, 395, 399, 406, 412, 415, 417, 436, 471, 566, 567, 593, 601, 646, 650, 652, 654, 658, 660, 662, 664, 672, 674, 797, 799, 800, 801, 802, 803, 811, 812, 813, 814, 816, 817, 819, 820, 821, 822, 823, 824, 825, 827, 831, 834, 881, 908, 909, 977, 980, 983, 984, 988, 998, 1009, 1019, 1020, 1024, 1033, 1037, 1038, 1039, 1040, 1041, 1043, 1045, 1047, 1048], "fold": [4, 68, 75, 79, 118, 121, 124, 138, 148, 170, 189, 248, 256, 257, 259, 261, 262, 264, 271, 313, 329, 347, 363, 376, 381, 384, 385, 394, 395, 399, 408, 417, 436, 471, 492, 535, 566, 567, 593, 601, 646, 650, 652, 654, 658, 660, 662, 664, 672, 674, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 827, 831, 834, 881, 902, 905, 977, 984, 988, 998, 1003, 1019, 1031, 1033, 1035, 1036, 1037, 1039, 1040, 1042, 1043, 1048], "1000": [4, 7, 8, 15, 27, 32, 38, 51, 73, 76, 86, 105, 115, 117, 119, 123, 124, 125, 129, 134, 140, 144, 158, 176, 186, 192, 203, 223, 232, 233, 235, 246, 250, 259, 261, 267, 270, 272, 289, 291, 307, 310, 317, 318, 320, 322, 340, 341, 348, 352, 353, 386, 387, 394, 406, 416, 436, 472, 478, 479, 530, 531, 536, 538, 541, 542, 544, 545, 547, 552, 563, 631, 645, 646, 650, 651, 652, 654, 659, 660, 661, 662, 665, 666, 667, 671, 675, 676, 677, 686, 691, 697, 700, 762, 877, 889, 895, 900, 901, 977, 981, 1003, 1019, 1022, 1038, 1040], "datapoint": [4, 43, 53, 57, 156, 159, 183, 234, 235, 239, 256, 261, 282, 345, 418, 445, 501, 556, 557, 558, 559, 563, 564, 565, 610, 908, 909, 910, 911, 981], "should": [4, 5, 6, 8, 9, 13, 15, 18, 26, 28, 32, 38, 54, 110, 112, 115, 118, 122, 125, 149, 164, 170, 176, 179, 180, 184, 200, 202, 218, 229, 234, 235, 236, 239, 261, 264, 267, 268, 270, 279, 284, 307, 308, 313, 328, 339, 347, 353, 363, 367, 368, 369, 376, 379, 380, 381, 383, 385, 386, 388, 389, 390, 394, 395, 398, 399, 402, 404, 406, 408, 410, 412, 413, 415, 416, 417, 418, 419, 420, 422, 425, 431, 436, 439, 441, 442, 443, 444, 446, 448, 449, 451, 453, 456, 457, 458, 461, 462, 463, 464, 466, 468, 469, 470, 471, 472, 473, 474, 475, 481, 482, 483, 484, 486, 495, 502, 507, 508, 515, 520, 522, 523, 526, 531, 532, 533, 535, 537, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 580, 581, 584, 585, 587, 588, 589, 590, 592, 593, 596, 601, 606, 607, 609, 610, 619, 626, 627, 628, 630, 631, 632, 634, 637, 638, 639, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 683, 686, 688, 689, 690, 691, 692, 693, 694, 695, 700, 708, 725, 726, 740, 764, 769, 772, 776, 777, 778, 779, 780, 783, 786, 787, 790, 798, 799, 804, 805, 806, 807, 814, 815, 816, 817, 822, 826, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 850, 851, 857, 858, 860, 863, 864, 865, 866, 867, 868, 869, 871, 872, 873, 874, 879, 880, 881, 883, 887, 890, 891, 892, 893, 894, 895, 896, 897, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 920, 924, 926, 929, 937, 945, 949, 954, 957, 959, 962, 963, 964, 965, 966, 967, 968, 969, 977, 978, 982, 983, 984, 985, 988, 989, 991, 993, 994, 995, 998, 1001, 1002, 1003, 1004, 1009, 1012, 1013, 1020, 1024, 1028, 1031, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049], "enough": [4, 13, 38, 45, 63, 118, 121, 152, 164, 171, 172, 189, 200, 234, 237, 239, 310, 352, 354, 363, 379, 381, 385, 396, 406, 407, 408, 410, 412, 415, 566, 567, 588, 797, 800, 801, 803, 811, 824, 937, 977, 982, 985, 987, 994, 998, 1003, 1008, 1013, 1037, 1047, 1048], "quantifi": [4, 38, 50, 79, 157, 218, 268, 383, 395, 403, 407, 572, 863, 983, 996, 998, 1026], "repres": [4, 8, 10, 12, 13, 15, 21, 27, 33, 35, 45, 49, 50, 51, 54, 92, 96, 98, 104, 105, 119, 120, 128, 170, 179, 187, 192, 202, 204, 205, 209, 223, 225, 234, 239, 249, 255, 258, 261, 264, 268, 289, 308, 343, 352, 354, 361, 369, 373, 376, 378, 383, 393, 394, 395, 396, 402, 406, 408, 412, 413, 414, 415, 416, 417, 437, 441, 445, 451, 462, 469, 470, 471, 472, 473, 474, 475, 487, 489, 490, 491, 493, 495, 497, 499, 500, 501, 503, 504, 509, 526, 533, 535, 537, 539, 540, 546, 554, 562, 580, 587, 590, 628, 631, 633, 639, 675, 677, 696, 698, 700, 728, 741, 787, 797, 799, 810, 811, 814, 817, 823, 825, 826, 829, 830, 836, 842, 843, 844, 846, 847, 848, 850, 851, 852, 857, 858, 867, 873, 874, 897, 900, 901, 920, 921, 941, 946, 978, 984, 985, 986, 987, 988, 989, 990, 991, 992, 994, 996, 998, 999, 1001, 1002, 1004, 1015, 1020, 1021, 1023, 1037, 1039, 1042, 1043, 1046], "bit": [4, 13, 49, 54, 79, 99, 198, 202, 279, 306, 339, 353, 363, 367, 376, 379, 415, 416, 581, 588, 657, 830, 920, 921, 985, 989, 1007, 1017, 1024, 1031, 1034, 1037, 1039, 1040, 1043, 1044, 1045, 1048], "less": [4, 5, 8, 53, 54, 61, 93, 96, 99, 106, 112, 119, 125, 126, 152, 159, 170, 182, 184, 200, 202, 204, 235, 239, 247, 249, 261, 262, 270, 272, 275, 307, 310, 311, 312, 318, 320, 343, 352, 353, 363, 367, 369, 376, 381, 383, 393, 395, 408, 412, 413, 415, 416, 420, 438, 440, 442, 444, 447, 449, 451, 456, 459, 461, 462, 481, 482, 483, 495, 514, 530, 534, 536, 537, 539, 540, 541, 542, 543, 544, 545, 546, 547, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 589, 595, 618, 626, 633, 646, 660, 675, 677, 691, 700, 720, 721, 722, 723, 734, 750, 762, 777, 778, 783, 793, 795, 796, 799, 800, 801, 807, 824, 835, 836, 837, 839, 840, 841, 842, 843, 844, 846, 848, 849, 850, 851, 852, 863, 875, 877, 878, 880, 883, 889, 890, 900, 901, 908, 909, 910, 911, 926, 940, 977, 978, 980, 981, 982, 985, 987, 988, 991, 993, 998, 1002, 1003, 1004, 1008, 1022, 1024, 1031, 1032, 1034, 1035, 1037, 1038, 1039, 1041, 1045, 1046, 1048], "than": [4, 5, 7, 11, 12, 14, 20, 28, 31, 32, 41, 45, 48, 54, 56, 73, 76, 81, 83, 87, 93, 96, 99, 101, 103, 112, 114, 115, 117, 118, 119, 121, 122, 125, 149, 152, 158, 161, 164, 170, 171, 172, 176, 179, 184, 186, 189, 200, 202, 206, 216, 217, 218, 225, 229, 235, 236, 237, 239, 247, 258, 259, 261, 262, 263, 264, 267, 270, 279, 282, 284, 290, 291, 303, 306, 310, 311, 312, 313, 318, 320, 322, 327, 343, 352, 353, 354, 363, 367, 368, 369, 375, 376, 377, 380, 381, 383, 386, 387, 389, 395, 396, 402, 404, 405, 406, 407, 408, 410, 411, 412, 413, 415, 416, 417, 418, 438, 439, 440, 441, 442, 444, 445, 446, 447, 448, 449, 451, 453, 456, 459, 462, 463, 466, 468, 469, 470, 471, 472, 473, 474, 475, 481, 482, 483, 492, 493, 495, 508, 513, 514, 522, 530, 533, 534, 535, 536, 537, 539, 540, 541, 543, 546, 547, 552, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 572, 580, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 609, 610, 626, 627, 629, 633, 639, 641, 642, 643, 644, 645, 646, 651, 652, 654, 657, 658, 659, 660, 661, 662, 665, 666, 667, 670, 671, 673, 675, 676, 677, 678, 681, 686, 691, 693, 695, 703, 704, 711, 719, 720, 721, 722, 733, 734, 737, 762, 777, 778, 779, 781, 783, 793, 795, 796, 797, 800, 801, 807, 811, 816, 818, 821, 822, 823, 824, 825, 828, 830, 831, 833, 834, 835, 836, 837, 839, 840, 841, 842, 843, 844, 846, 847, 848, 849, 850, 851, 852, 854, 857, 858, 863, 873, 874, 875, 877, 878, 880, 883, 889, 890, 894, 897, 900, 901, 902, 905, 906, 908, 909, 910, 911, 912, 926, 937, 939, 940, 945, 951, 957, 959, 962, 977, 978, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 994, 995, 996, 998, 1002, 1003, 1004, 1008, 1009, 1015, 1019, 1022, 1023, 1024, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048], "half": [4, 25, 99, 234, 238, 259, 261, 273, 376, 377, 413, 462, 521, 561, 592, 596, 601, 768, 859, 867, 988, 991, 1004], "contigu": [4, 22, 383, 393, 405, 412, 416, 442, 446, 448, 458, 645, 646, 651, 652, 659, 660, 661, 662, 680, 683, 779, 816, 840, 841, 900, 902, 903, 904, 905, 906, 1003, 1034, 1041, 1045], "model_select": [4, 5, 6, 7, 10, 13, 25, 26, 28, 32, 33, 55, 72, 73, 74, 75, 76, 79, 87, 101, 103, 112, 117, 118, 119, 122, 123, 124, 125, 126, 127, 128, 131, 132, 138, 139, 145, 146, 148, 152, 164, 165, 167, 169, 170, 172, 173, 176, 184, 195, 200, 202, 207, 208, 215, 216, 218, 230, 235, 239, 241, 242, 243, 248, 251, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 279, 282, 286, 287, 288, 292, 293, 300, 302, 303, 308, 310, 311, 312, 313, 316, 317, 318, 321, 323, 329, 330, 339, 342, 347, 357, 361, 363, 370, 381, 383, 386, 394, 395, 399, 404, 409, 412, 415, 436, 437, 557, 559, 566, 567, 578, 658, 696, 697, 699, 701, 740, 828, 831, 849, 857, 858, 860, 908, 909, 910, 911, 956, 977, 978, 983, 988, 990, 991, 996, 998, 1003, 1009, 1010, 1019, 1020, 1024, 1028, 1035, 1037, 1038], "timeseriessplit": [4, 13, 184, 256, 412, 1037, 1038, 1043], "ts_cv": [4, 13], "n_split": [4, 13, 118, 124, 164, 165, 170, 248, 256, 261, 263, 266, 271, 279, 323, 329, 339, 347, 363, 412, 798, 799, 800, 801, 802, 804, 805, 806, 807, 810, 812, 813, 814, 815, 816, 817, 818, 823, 1019, 1037, 1039], "5": [4, 6, 7, 8, 10, 11, 12, 13, 14, 15, 17, 18, 20, 22, 25, 26, 27, 32, 33, 35, 36, 38, 39, 40, 41, 43, 44, 45, 46, 47, 48, 51, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 65, 66, 67, 68, 71, 72, 74, 75, 76, 80, 81, 82, 83, 84, 86, 87, 93, 96, 97, 98, 99, 100, 102, 103, 105, 110, 114, 115, 116, 117, 118, 120, 121, 123, 124, 125, 126, 127, 128, 129, 130, 131, 134, 138, 139, 141, 144, 148, 149, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 164, 165, 169, 170, 171, 172, 173, 174, 176, 180, 181, 182, 183, 184, 186, 188, 189, 190, 191, 192, 194, 195, 197, 198, 200, 201, 202, 203, 206, 208, 211, 212, 213, 214, 215, 216, 218, 222, 223, 224, 226, 229, 232, 233, 234, 235, 237, 238, 239, 240, 241, 243, 246, 247, 248, 249, 250, 251, 252, 256, 258, 259, 260, 261, 262, 263, 264, 266, 267, 268, 270, 272, 274, 275, 276, 279, 282, 284, 286, 287, 288, 289, 290, 291, 293, 294, 296, 297, 300, 301, 302, 303, 306, 308, 309, 310, 311, 313, 316, 317, 318, 319, 320, 321, 322, 323, 326, 327, 329, 330, 331, 337, 338, 339, 340, 341, 343, 344, 346, 347, 348, 349, 352, 353, 354, 357, 358, 359, 360, 361, 362, 363, 376, 378, 379, 381, 383, 386, 387, 393, 394, 395, 398, 400, 401, 406, 408, 409, 412, 413, 415, 416, 417, 418, 419, 420, 436, 437, 438, 439, 441, 443, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 460, 461, 462, 463, 465, 468, 471, 477, 481, 482, 483, 484, 489, 492, 493, 503, 515, 516, 520, 522, 523, 525, 527, 530, 532, 533, 535, 536, 537, 538, 539, 542, 543, 544, 545, 546, 551, 556, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 569, 581, 591, 592, 593, 596, 601, 603, 604, 605, 609, 612, 613, 614, 615, 618, 619, 621, 622, 624, 626, 627, 628, 629, 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627, 629, 630, 631, 632, 696, 697, 699, 700, 701, 780, 787, 797, 799, 803, 807, 811, 815, 819, 821, 822, 830, 861, 873, 902, 905, 908, 909, 910, 911, 915, 918, 940, 941, 947, 956, 961, 973, 978, 984, 987, 988, 989, 991, 995, 998, 999, 1000, 1002, 1003, 1004, 1008, 1009, 1013, 1020, 1022, 1023, 1028, 1031, 1032, 1034, 1037, 1041, 1044, 1045, 1046, 1047, 1048], "turn": [4, 33, 79, 164, 165, 170, 184, 215, 216, 234, 261, 288, 354, 367, 369, 376, 393, 395, 396, 407, 408, 409, 411, 413, 416, 417, 418, 580, 581, 588, 606, 607, 829, 833, 902, 905, 923, 978, 982, 990, 993, 998, 1013, 1024, 1036, 1038, 1039, 1044], "downstream": [4, 81, 313, 354, 416, 530, 533, 536, 540, 541, 605, 873, 998, 1041], "freedom": [4, 98, 157, 188, 189, 191, 261, 540, 655, 795, 984, 1034], "underfit": [4, 35, 125, 152, 167, 172, 201, 204, 254, 276, 277, 300, 312, 347, 415, 656, 797, 800, 801, 811, 822, 823, 860, 875, 983, 984, 1010], "approxim": [4, 5, 28, 34, 47, 54, 81, 100, 101, 105, 116, 124, 125, 149, 153, 170, 177, 180, 196, 200, 201, 214, 218, 223, 226, 228, 229, 232, 235, 244, 252, 261, 266, 275, 285, 286, 293, 298, 306, 308, 309, 311, 316, 319, 352, 354, 359, 360, 363, 370, 373, 378, 390, 393, 395, 405, 406, 409, 411, 412, 413, 415, 418, 420, 421, 422, 432, 448, 468, 469, 472, 473, 474, 475, 481, 482, 483, 490, 495, 501, 520, 523, 532, 533, 534, 535, 537, 538, 539, 540, 542, 543, 544, 545, 546, 558, 559, 581, 588, 609, 610, 618, 637, 638, 639, 640, 641, 656, 657, 658, 671, 673, 675, 676, 678, 686, 691, 756, 757, 762, 795, 798, 815, 821, 825, 826, 840, 841, 844, 848, 850, 851, 852, 856, 860, 861, 866, 870, 872, 873, 877, 889, 900, 905, 906, 936, 937, 962, 981, 982, 983, 984, 985, 987, 991, 992, 993, 994, 995, 996, 1000, 1002, 1003, 1004, 1008, 1009, 1010, 1024, 1026, 1031, 1032, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1043, 1044, 1045], "especi": [4, 8, 13, 56, 57, 122, 125, 170, 200, 236, 312, 321, 353, 363, 368, 375, 381, 389, 393, 395, 406, 408, 416, 442, 448, 500, 537, 556, 557, 558, 559, 563, 564, 565, 645, 646, 647, 649, 651, 652, 653, 657, 658, 659, 660, 661, 662, 668, 679, 694, 704, 744, 835, 836, 837, 838, 839, 908, 909, 910, 911, 984, 990, 991, 996, 998, 1013, 1034, 1039, 1043, 1044, 1045], "sharp": [4, 171, 995, 998], "rush": [4, 13, 381], "flatter": [4, 26], "accur": [4, 9, 13, 22, 26, 27, 81, 96, 127, 152, 159, 170, 171, 204, 215, 218, 261, 268, 272, 279, 320, 352, 353, 363, 380, 406, 408, 410, 413, 415, 416, 417, 436, 442, 446, 450, 452, 458, 530, 537, 539, 541, 542, 544, 545, 546, 606, 607, 633, 800, 801, 840, 841, 936, 937, 980, 985, 988, 1004, 1045], "tend": [4, 12, 28, 38, 40, 44, 53, 56, 58, 149, 170, 171, 189, 200, 205, 218, 246, 249, 258, 262, 282, 303, 343, 354, 367, 369, 381, 383, 395, 406, 408, 410, 413, 415, 417, 436, 447, 652, 846, 877, 889, 937, 984, 985, 991, 1004, 1024], "under": [4, 7, 11, 25, 26, 28, 42, 68, 79, 80, 103, 122, 125, 171, 200, 229, 239, 261, 268, 270, 271, 307, 322, 370, 375, 381, 383, 385, 387, 389, 395, 398, 402, 406, 408, 410, 412, 413, 416, 417, 445, 450, 452, 469, 470, 471, 472, 473, 474, 475, 531, 538, 540, 542, 653, 654, 655, 701, 705, 706, 707, 714, 740, 752, 786, 787, 795, 796, 845, 944, 984, 985, 986, 988, 991, 998, 1002, 1003, 1004, 1008, 1009, 1024, 1031, 1033, 1034, 1036, 1039, 1043], "These": [4, 5, 7, 28, 52, 96, 98, 117, 121, 167, 171, 189, 200, 201, 202, 230, 268, 279, 284, 288, 303, 306, 313, 315, 320, 327, 329, 363, 367, 368, 374, 377, 378, 381, 383, 385, 388, 390, 393, 395, 398, 402, 405, 406, 407, 408, 411, 413, 415, 416, 417, 418, 441, 445, 447, 497, 507, 508, 514, 537, 566, 567, 601, 602, 667, 675, 676, 698, 705, 731, 776, 792, 797, 800, 801, 803, 811, 819, 821, 822, 823, 824, 825, 827, 845, 846, 865, 977, 979, 982, 984, 985, 988, 989, 999, 1001, 1003, 1008, 1015, 1019, 1024, 1031, 1037, 1039, 1040, 1041, 1044, 1048], "systemat": [4, 261, 383, 711, 726, 988, 1046], "reveal": [4, 13, 170, 172, 352, 387, 405, 985, 1047], "form": [4, 9, 20, 33, 43, 61, 68, 96, 135, 159, 160, 170, 235, 236, 252, 261, 326, 363, 375, 378, 381, 383, 385, 395, 396, 405, 407, 408, 411, 412, 413, 414, 415, 416, 418, 420, 422, 436, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 455, 458, 462, 463, 464, 466, 468, 469, 470, 471, 472, 473, 474, 475, 481, 482, 483, 484, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 569, 580, 581, 582, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 601, 602, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 626, 627, 628, 629, 634, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 681, 682, 684, 686, 687, 688, 689, 690, 691, 692, 698, 795, 796, 797, 800, 801, 802, 805, 807, 811, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 856, 857, 858, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 892, 893, 895, 896, 897, 900, 901, 902, 903, 904, 905, 906, 908, 909, 910, 911, 926, 945, 948, 977, 979, 981, 982, 984, 985, 988, 989, 992, 993, 994, 998, 1003, 1008, 1015, 1022, 1037, 1041, 1043], "explain": [4, 5, 12, 57, 74, 86, 87, 104, 125, 152, 157, 158, 171, 172, 200, 218, 229, 236, 252, 271, 274, 312, 352, 353, 368, 379, 381, 385, 402, 404, 411, 413, 418, 492, 520, 523, 533, 540, 543, 548, 624, 647, 655, 668, 679, 720, 721, 722, 726, 783, 984, 991, 1004, 1007, 1008, 1020, 1022, 1023, 1042, 1044, 1045], "lack": [4, 343, 381, 383, 393, 412, 825, 915, 1003, 1045], "deriv": [4, 20, 105, 106, 124, 157, 160, 172, 188, 189, 193, 201, 368, 376, 383, 410, 412, 414, 415, 416, 418, 420, 440, 444, 445, 464, 532, 605, 616, 621, 647, 668, 675, 679, 857, 858, 873, 879, 900, 901, 982, 984, 985, 988, 992, 994, 1003, 1031, 1032, 1033, 1038, 1039, 1040, 1046], "address": [4, 49, 204, 352, 376, 381, 408, 416, 446, 656, 984, 985, 990, 991, 992, 993, 994, 998, 1013, 1022, 1039], "section": [4, 38, 96, 101, 115, 123, 132, 138, 139, 140, 141, 157, 161, 169, 170, 171, 173, 179, 184, 188, 189, 202, 225, 230, 236, 239, 241, 259, 261, 264, 270, 311, 312, 313, 318, 352, 353, 354, 363, 374, 376, 379, 381, 382, 383, 385, 386, 387, 388, 389, 396, 399, 402, 406, 407, 409, 411, 412, 413, 415, 416, 417, 418, 438, 442, 458, 533, 613, 618, 621, 636, 644, 786, 796, 902, 905, 977, 982, 984, 988, 989, 991, 992, 994, 995, 1002, 1003, 1005, 1007, 1022, 1024, 1031, 1033, 1034, 1035, 1038], "margin": [4, 12, 17, 26, 28, 92, 114, 153, 154, 155, 156, 157, 158, 167, 170, 171, 178, 210, 211, 213, 219, 237, 239, 261, 279, 306, 309, 312, 334, 335, 337, 338, 339, 341, 343, 348, 349, 350, 378, 406, 407, 413, 415, 418, 511, 609, 610, 630, 644, 675, 733, 829, 877, 889, 902, 905, 984, 988, 990, 994, 995, 998, 1002, 1003, 1010, 1022, 1038, 1040], "construct": [4, 11, 71, 73, 112, 114, 116, 120, 132, 149, 218, 230, 236, 241, 243, 297, 307, 309, 317, 320, 375, 378, 383, 390, 395, 408, 409, 412, 413, 415, 416, 419, 433, 440, 441, 443, 444, 449, 451, 456, 462, 464, 466, 518, 534, 540, 543, 554, 555, 580, 586, 638, 687, 689, 690, 811, 840, 841, 842, 843, 844, 846, 848, 850, 851, 852, 859, 860, 861, 862, 864, 905, 919, 921, 936, 937, 946, 947, 951, 977, 978, 980, 984, 985, 988, 989, 991, 998, 1001, 1003, 1004, 1009, 1015, 1019, 1036, 1038, 1039, 1042, 1045], "polynomialfeatur": [4, 179, 200, 201, 206, 241, 275, 318, 343, 879, 984, 998, 1035, 1039, 1040, 1041, 1044, 1045, 1047, 1048], "coars": [4, 121, 387, 447, 460], "explicitli": [4, 47, 119, 152, 159, 164, 165, 201, 232, 236, 323, 343, 354, 363, 368, 375, 377, 381, 382, 383, 385, 393, 395, 399, 404, 412, 416, 418, 534, 548, 549, 578, 579, 596, 626, 647, 657, 658, 668, 679, 708, 725, 780, 786, 787, 800, 801, 831, 834, 840, 841, 866, 890, 891, 956, 977, 978, 980, 982, 984, 985, 988, 990, 991, 998, 1013, 1024, 1031, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1043, 1044, 1045], "too": [4, 7, 12, 13, 18, 45, 51, 56, 58, 63, 71, 75, 83, 100, 125, 164, 172, 184, 201, 204, 222, 233, 239, 247, 255, 259, 268, 276, 302, 312, 339, 352, 359, 360, 367, 378, 381, 387, 389, 393, 395, 408, 410, 412, 415, 416, 417, 420, 436, 448, 537, 587, 590, 678, 691, 706, 835, 836, 837, 838, 839, 865, 878, 880, 983, 985, 987, 988, 990, 991, 995, 1022, 1032, 1035, 1038, 1039, 1040, 1041, 1043], "mani": [4, 7, 10, 20, 56, 57, 75, 79, 83, 112, 118, 119, 120, 121, 126, 145, 149, 172, 200, 221, 233, 247, 255, 261, 268, 269, 273, 284, 306, 311, 312, 316, 317, 318, 319, 320, 322, 323, 352, 363, 367, 369, 376, 378, 380, 381, 394, 395, 396, 398, 399, 405, 406, 408, 409, 410, 413, 415, 416, 417, 418, 420, 502, 552, 553, 556, 557, 558, 559, 563, 564, 565, 587, 590, 605, 608, 626, 638, 649, 653, 657, 658, 665, 666, 667, 675, 676, 677, 691, 694, 772, 776, 797, 801, 811, 857, 858, 873, 878, 880, 900, 908, 909, 910, 911, 977, 978, 980, 984, 985, 987, 988, 989, 990, 991, 994, 995, 996, 998, 1001, 1003, 1005, 1007, 1008, 1009, 1013, 1022, 1024, 1031, 1033, 1034, 1036, 1037, 1038, 1039, 1040, 1041, 1045, 1046, 1047, 1048], "featureunion": [4, 75, 373, 395, 399, 407, 463, 628, 862, 978, 1009, 1013, 1026, 1032, 1035, 1036, 1037, 1039, 1040, 1041, 1042, 1043, 1045, 1046, 1047], "hour_workday_interact": 4, "interaction_onli": [4, 875, 984, 998], "combin": [4, 12, 13, 21, 26, 28, 62, 71, 72, 74, 75, 76, 91, 96, 104, 105, 111, 122, 123, 127, 135, 136, 152, 158, 164, 165, 167, 170, 184, 200, 204, 217, 218, 225, 229, 231, 234, 239, 257, 261, 262, 270, 272, 273, 293, 301, 312, 318, 319, 321, 342, 343, 354, 358, 367, 373, 377, 378, 381, 386, 394, 395, 405, 406, 407, 408, 409, 412, 413, 414, 415, 416, 417, 418, 420, 421, 444, 463, 465, 466, 472, 474, 475, 478, 479, 480, 488, 495, 507, 508, 514, 520, 523, 525, 530, 536, 537, 539, 541, 546, 548, 559, 561, 564, 566, 567, 609, 613, 615, 620, 623, 629, 633, 642, 645, 646, 651, 652, 657, 658, 659, 660, 661, 662, 665, 667, 671, 672, 673, 674, 675, 677, 678, 680, 683, 691, 700, 728, 786, 797, 800, 801, 808, 809, 821, 823, 827, 844, 859, 860, 861, 865, 873, 874, 875, 880, 900, 962, 977, 980, 981, 984, 988, 989, 991, 994, 998, 1001, 1002, 1004, 1005, 1010, 1013, 1020, 1022, 1024, 1031, 1036, 1039, 1042, 1043, 1044, 1046, 1047], "previou": [4, 44, 54, 72, 96, 115, 122, 123, 125, 132, 152, 157, 158, 169, 170, 171, 184, 186, 201, 233, 235, 236, 261, 264, 268, 306, 311, 320, 352, 353, 354, 363, 367, 376, 381, 383, 386, 387, 389, 393, 395, 396, 398, 408, 415, 416, 467, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 629, 645, 647, 651, 652, 657, 659, 661, 665, 666, 667, 668, 675, 676, 677, 679, 706, 816, 831, 834, 849, 857, 858, 897, 914, 984, 988, 992, 998, 1009, 1015, 1024, 1031, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048], "nice": [4, 68, 201, 236, 306, 367, 413, 1024], "cyclic_spline_interactions_pipelin": 4, "078": [4, 102, 107, 1010], "009": [4, 353, 387], "104": [4, 13, 25], "highlight": [4, 13, 15, 56, 72, 81, 93, 110, 116, 117, 118, 119, 120, 122, 129, 146, 149, 152, 153, 158, 164, 165, 171, 172, 176, 181, 188, 200, 201, 202, 218, 231, 236, 240, 241, 242, 243, 256, 257, 258, 270, 273, 279, 282, 286, 310, 312, 324, 352, 354, 370, 381, 385, 386, 389, 393, 418, 442, 445, 446, 463, 466, 489, 490, 495, 500, 501, 503, 511, 514, 520, 523, 537, 560, 561, 563, 564, 566, 598, 601, 627, 629, 631, 633, 639, 645, 647, 651, 657, 668, 679, 687, 700, 701, 716, 730, 746, 786, 793, 797, 798, 800, 801, 803, 811, 819, 822, 823, 826, 842, 844, 861, 865, 870, 873, 874, 875, 879, 880, 881, 897, 898, 900, 905, 908, 909, 932, 985, 988, 996, 998, 1003, 1010, 1041, 1042, 1043, 1044, 1045, 1046, 1047], "anoth": [4, 38, 96, 101, 112, 123, 127, 149, 152, 160, 165, 170, 189, 204, 208, 226, 229, 236, 240, 247, 261, 263, 264, 267, 271, 294, 352, 354, 357, 363, 367, 369, 375, 376, 379, 381, 383, 387, 393, 395, 399, 402, 405, 408, 412, 413, 415, 416, 417, 419, 441, 443, 451, 454, 461, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 507, 537, 539, 546, 672, 675, 691, 708, 718, 803, 819, 859, 860, 881, 977, 978, 983, 984, 985, 988, 989, 991, 994, 998, 1002, 1003, 1015, 1022, 1023, 1024, 1040, 1041], "either": [4, 10, 31, 72, 118, 121, 122, 170, 188, 190, 230, 239, 256, 265, 270, 289, 347, 352, 361, 367, 368, 373, 376, 379, 381, 382, 383, 387, 395, 399, 402, 406, 408, 409, 411, 412, 413, 414, 415, 416, 417, 418, 420, 441, 446, 451, 486, 495, 511, 523, 532, 533, 535, 543, 554, 555, 556, 557, 558, 559, 560, 561, 563, 564, 565, 566, 567, 581, 582, 583, 587, 588, 589, 590, 592, 593, 596, 601, 606, 607, 609, 610, 621, 626, 629, 631, 632, 649, 653, 655, 663, 675, 677, 681, 682, 689, 696, 697, 701, 705, 706, 710, 711, 719, 722, 724, 725, 726, 727, 728, 733, 736, 737, 738, 740, 750, 754, 767, 772, 776, 780, 781, 782, 783, 785, 786, 787, 792, 797, 800, 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554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 572, 581, 582, 583, 588, 606, 607, 609, 610, 626, 629, 631, 633, 638, 639, 640, 641, 642, 645, 646, 648, 649, 651, 652, 653, 657, 659, 660, 661, 662, 669, 670, 671, 675, 676, 677, 678, 688, 689, 690, 691, 692, 693, 694, 700, 703, 704, 707, 729, 741, 779, 791, 795, 796, 797, 799, 800, 801, 802, 809, 811, 812, 813, 814, 815, 816, 817, 818, 825, 826, 831, 833, 834, 835, 836, 837, 839, 840, 841, 845, 849, 856, 857, 858, 860, 863, 865, 874, 877, 881, 889, 892, 893, 894, 895, 896, 897, 900, 901, 902, 903, 905, 906, 908, 909, 910, 911, 923, 936, 937, 959, 962, 978, 980, 983, 985, 987, 988, 992, 993, 994, 996, 998, 1003, 1004, 1008, 1009, 1010, 1011, 1015, 1019, 1020, 1022, 1023, 1026, 1028, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048], "constant": [4, 105, 115, 155, 159, 165, 170, 188, 201, 202, 204, 218, 231, 236, 241, 264, 296, 301, 303, 307, 309, 317, 347, 349, 363, 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808, 811, 895, 896, 897, 902, 903, 904, 905, 906, 977, 980, 983, 984, 986, 1001, 1003, 1015, 1019, 1020, 1022, 1031, 1032, 1034, 1035, 1036, 1038, 1039, 1041, 1042, 1045, 1046, 1047], "achiev": [4, 12, 18, 20, 28, 32, 99, 112, 121, 123, 130, 160, 176, 200, 208, 258, 261, 268, 311, 329, 339, 342, 343, 347, 352, 363, 367, 369, 375, 376, 378, 383, 387, 393, 395, 405, 406, 408, 412, 413, 415, 416, 552, 557, 564, 639, 648, 675, 677, 706, 727, 728, 736, 738, 761, 781, 782, 785, 800, 801, 836, 880, 977, 978, 980, 984, 988, 998, 1001, 1003, 1012, 1024, 1044], "gridsearchcv": [4, 55, 70, 72, 74, 75, 77, 79, 100, 103, 118, 167, 235, 241, 251, 254, 259, 260, 261, 262, 266, 269, 272, 277, 279, 286, 288, 303, 308, 318, 322, 323, 339, 343, 363, 368, 383, 393, 395, 399, 409, 412, 418, 422, 501, 519, 539, 540, 574, 598, 606, 652, 702, 740, 808, 811, 860, 870, 900, 908, 956, 977, 984, 988, 992, 1002, 1003, 1008, 1009, 1010, 1019, 1020, 1024, 1031, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 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873, 874, 877, 879, 880, 885, 886, 887, 900, 901, 988, 989, 998, 1003, 1010, 1019, 1024, 1026, 1032, 1035, 1037, 1043, 1045, 1047], "common": [4, 13, 15, 18, 20, 99, 146, 149, 167, 168, 169, 174, 196, 204, 205, 256, 261, 268, 279, 318, 343, 352, 354, 361, 367, 370, 375, 381, 383, 389, 391, 393, 394, 397, 399, 405, 406, 407, 408, 409, 412, 414, 417, 418, 446, 464, 466, 495, 589, 631, 652, 671, 672, 700, 744, 751, 767, 798, 799, 802, 812, 814, 815, 816, 817, 818, 823, 826, 861, 863, 871, 872, 873, 878, 880, 885, 886, 888, 889, 890, 891, 939, 977, 978, 984, 991, 994, 998, 1003, 1004, 1008, 1009, 1010, 1013, 1015, 1026, 1031, 1039, 1041, 1042, 1046], "pitfal": [4, 149, 167, 168, 169, 172, 174, 196, 204, 205, 261, 318, 370, 394, 395, 397, 412, 464, 466, 495, 652, 671, 672, 700, 751, 812, 823, 826, 861, 873, 880, 984, 1010, 1026], "coeffici": [4, 7, 10, 39, 50, 59, 61, 79, 80, 83, 96, 99, 138, 146, 152, 167, 168, 169, 174, 178, 182, 184, 185, 186, 187, 191, 193, 194, 195, 196, 197, 198, 202, 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38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 71, 72, 73, 74, 75, 76, 79, 80, 81, 82, 83, 86, 87, 90, 91, 92, 93, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 110, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 138, 139, 140, 141, 144, 145, 146, 147, 148, 149, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 164, 165, 167, 169, 170, 171, 172, 173, 176, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 221, 222, 223, 224, 225, 226, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 246, 247, 248, 249, 250, 251, 252, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 279, 282, 284, 286, 287, 288, 289, 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984, 985, 991, 1036, 1041, 1042, 1043, 1044, 1045, 1046, 1047], "take": [5, 8, 13, 21, 31, 47, 54, 56, 58, 67, 76, 81, 93, 96, 112, 121, 152, 154, 157, 161, 170, 176, 183, 204, 208, 209, 225, 236, 248, 249, 256, 262, 266, 270, 271, 282, 284, 287, 290, 291, 292, 294, 295, 306, 320, 329, 335, 336, 339, 349, 353, 354, 358, 367, 368, 369, 375, 376, 381, 383, 386, 387, 388, 389, 393, 395, 396, 406, 408, 409, 411, 412, 413, 415, 416, 417, 418, 420, 436, 442, 445, 446, 448, 449, 451, 456, 457, 458, 460, 462, 468, 518, 532, 537, 538, 539, 545, 546, 580, 591, 594, 595, 597, 598, 599, 609, 610, 615, 619, 620, 623, 631, 642, 648, 670, 672, 673, 674, 675, 678, 690, 691, 698, 704, 706, 708, 718, 727, 728, 736, 740, 741, 754, 769, 772, 776, 777, 778, 779, 781, 782, 785, 786, 797, 798, 802, 803, 811, 815, 824, 842, 843, 844, 846, 848, 849, 850, 851, 852, 864, 874, 895, 896, 900, 901, 902, 903, 904, 905, 906, 924, 958, 977, 980, 984, 985, 987, 988, 989, 991, 993, 994, 995, 998, 1003, 1004, 1013, 1015, 1019, 1021, 1028, 1031, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1048], "exact": [5, 7, 83, 101, 118, 157, 204, 234, 284, 323, 363, 367, 368, 375, 381, 383, 390, 393, 394, 395, 408, 415, 417, 433, 481, 482, 483, 487, 495, 533, 534, 540, 560, 561, 637, 641, 645, 691, 797, 811, 821, 822, 823, 902, 905, 977, 980, 984, 985, 994, 998, 1002, 1003, 1038, 1039, 1041], "usp": 5, "digit": [5, 12, 13, 30, 36, 37, 39, 40, 42, 44, 46, 47, 49, 50, 52, 54, 57, 58, 60, 62, 63, 69, 73, 74, 86, 89, 94, 99, 117, 137, 138, 142, 147, 149, 167, 190, 191, 207, 215, 216, 220, 221, 223, 225, 227, 233, 234, 238, 249, 255, 259, 260, 263, 266, 275, 276, 284, 288, 293, 294, 299, 301, 302, 304, 322, 325, 328, 332, 342, 353, 374, 381, 387, 408, 414, 415, 417, 440, 444, 446, 501, 540, 543, 548, 565, 657, 687, 688, 689, 690, 691, 696, 703, 704, 712, 716, 717, 735, 791, 793, 822, 826, 842, 849, 856, 860, 861, 870, 880, 886, 893, 896, 905, 912, 913, 914, 977, 983, 985, 988, 991, 993, 1001, 1010, 1015, 1019, 1022, 1023, 1026, 1035], "reproduc": [5, 12, 61, 112, 170, 176, 188, 270, 279, 303, 368, 370, 380, 381, 383, 384, 389, 393, 395, 402, 408, 412, 413, 416, 420, 439, 453, 459, 468, 473, 487, 490, 491, 494, 496, 502, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 534, 535, 536, 537, 538, 539, 540, 542, 543, 544, 545, 546, 550, 552, 553, 554, 555, 558, 559, 560, 561, 562, 606, 607, 609, 610, 633, 638, 639, 640, 641, 645, 646, 649, 651, 652, 653, 659, 660, 661, 662, 665, 666, 667, 670, 675, 677, 678, 688, 689, 691, 692, 693, 791, 795, 796, 799, 800, 801, 802, 803, 809, 811, 812, 813, 814, 815, 816, 817, 824, 825, 826, 830, 831, 834, 845, 849, 856, 857, 858, 865, 877, 881, 889, 892, 893, 900, 901, 902, 905, 936, 937, 959, 962, 980, 1008, 1012, 1031, 1039, 1044, 1045], "sect": 5, "bak\u0131r": [5, 413, 534], "g\u00f6khan": [5, 413, 534], "h": [5, 6, 49, 59, 96, 97, 112, 113, 130, 149, 154, 170, 248, 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534, 537, 539, 546, 566, 567, 638, 644, 695, 739, 795, 849, 856, 857, 858, 866, 980, 984, 985, 986, 988, 991, 994, 998, 1002, 1004, 1008, 1011, 1013, 1020, 1025, 1026, 1031, 1043], "system": [5, 101, 184, 261, 268, 363, 367, 368, 369, 376, 378, 381, 382, 383, 384, 389, 393, 398, 408, 413, 415, 419, 443, 470, 471, 477, 534, 538, 542, 549, 638, 644, 649, 650, 653, 654, 655, 681, 682, 724, 754, 767, 795, 849, 895, 896, 956, 984, 988, 991, 1000, 1008, 1009, 1013, 1024, 1031, 1039, 1040], "2004": [5, 149, 261, 279, 376, 378, 408, 410, 413, 472, 534, 606, 607, 688, 692, 704, 741, 781, 896, 982, 984, 985, 990, 1003, 1022], "449": [5, 413, 534], "456": [5, 413, 534], "author": [5, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 21, 22, 25, 26, 27, 28, 31, 33, 38, 40, 41, 43, 47, 48, 49, 53, 54, 55, 57, 58, 62, 66, 67, 68, 71, 72, 73, 75, 76, 83, 96, 98, 100, 101, 103, 106, 110, 113, 114, 115, 116, 117, 118, 124, 126, 127, 131, 132, 149, 152, 153, 155, 156, 157, 158, 159, 161, 165, 176, 179, 180, 182, 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699, 701, 740, 988, 1048], "decision_funct": [9, 11, 17, 26, 28, 57, 114, 128, 141, 212, 213, 214, 230, 236, 239, 268, 290, 291, 300, 308, 323, 337, 338, 339, 343, 344, 345, 349, 383, 395, 406, 415, 436, 468, 548, 549, 552, 554, 558, 560, 562, 566, 592, 593, 630, 631, 632, 657, 658, 665, 667, 673, 674, 675, 676, 697, 699, 701, 706, 719, 724, 725, 733, 737, 738, 740, 754, 780, 786, 787, 792, 797, 800, 801, 811, 821, 828, 829, 830, 831, 846, 860, 867, 897, 900, 902, 904, 905, 984, 988, 994, 995, 1002, 1003, 1022, 1031, 1033, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1043, 1044, 1048], "plot_method": [9, 35, 183, 214, 287, 292, 335, 337, 338, 340, 341, 343, 630], "contour": [9, 11, 17, 35, 47, 48, 81, 121, 141, 149, 155, 156, 158, 211, 212, 213, 214, 229, 234, 250, 290, 297, 337, 338, 340, 341, 343, 344, 345, 378, 388, 630, 631, 994, 1002], "line2d": [9, 81, 214, 290, 338, 341, 803, 819], "bbox_arg": 9, "dict": [9, 10, 11, 12, 18, 27, 40, 46, 71, 74, 75, 102, 118, 123, 125, 127, 198, 206, 229, 238, 248, 259, 260, 262, 265, 268, 270, 284, 288, 308, 312, 320, 330, 339, 353, 354, 369, 373, 375, 383, 393, 395, 409, 412, 419, 420, 422, 426, 430, 432, 436, 437, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 456, 463, 464, 468, 469, 470, 471, 472, 473, 474, 475, 481, 482, 483, 484, 495, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 580, 581, 582, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 609, 610, 611, 612, 613, 614, 615, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 633, 634, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 687, 688, 689, 690, 691, 696, 697, 699, 700, 701, 712, 760, 763, 769, 777, 778, 795, 796, 797, 800, 801, 803, 808, 809, 811, 819, 821, 822, 823, 824, 825, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 856, 857, 858, 859, 860, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 892, 893, 895, 896, 897, 900, 901, 902, 903, 904, 905, 906, 908, 909, 910, 911, 915, 925, 926, 942, 945, 948, 977, 988, 998, 1019, 1020, 1024, 1026, 1031, 1037, 1038, 1047, 1048], "boxstyl": [9, 308], "fc": [9, 289, 376], "arrow_arg": 9, "arrowstyl": 9, "xy": [9, 27, 50, 268, 297, 344, 398, 645, 646, 649, 650, 651, 652, 653, 654, 659, 660, 661, 662, 663, 680, 681, 682, 683, 685], "xycoord": [9, 27], "textcoord": [9, 176], "xytext": [9, 27, 176], "bbox": [9, 12, 35, 46, 102, 246, 247, 248, 251, 252, 308], "arrowprop": [9, 27], "ash": [9, 378, 1034, 1037], "malic_acid": 9, "abil": [9, 13, 62, 81, 97, 125, 151, 152, 153, 157, 159, 160, 161, 162, 164, 167, 169, 170, 172, 200, 204, 218, 229, 352, 369, 398, 415, 416, 418, 610, 621, 624, 780, 781, 782, 785, 985, 988, 991, 994, 1004, 1009, 1010, 1031, 1033, 1038, 1040, 1044, 1045], "locat": [9, 26, 54, 55, 73, 81, 82, 194, 297, 318, 339, 374, 379, 381, 385, 386, 389, 390, 398, 408, 410, 414, 447, 459, 460, 468, 469, 470, 471, 472, 473, 474, 475, 487, 499, 500, 503, 504, 511, 514, 973, 978, 984, 989, 994, 998, 1004, 1039], "hard": [9, 200, 229, 233, 261, 263, 369, 380, 393, 395, 408, 413, 416, 533, 540, 568, 675, 902, 903, 904, 905, 906, 984, 1001, 1003, 1004, 1009, 1022, 1023, 1024, 1040, 1041], "due": [9, 13, 27, 28, 54, 56, 61, 76, 112, 115, 152, 170, 171, 179, 184, 191, 198, 200, 202, 217, 218, 233, 239, 247, 269, 270, 282, 284, 311, 312, 343, 347, 352, 353, 354, 379, 381, 387, 395, 398, 406, 408, 412, 413, 415, 418, 442, 445, 446, 458, 534, 538, 542, 543, 573, 670, 797, 800, 801, 805, 807, 811, 821, 822, 825, 828, 832, 833, 865, 869, 870, 880, 978, 984, 985, 987, 988, 989, 991, 995, 1002, 1003, 1009, 1023, 1031, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048], "banana": [9, 988], "anywai": [9, 352, 383, 664, 684, 685], "rid": [9, 1002], "difficulti": [9, 28, 218, 306, 369, 381, 406, 857, 858, 987, 991], "bandwidth": [9, 45, 64, 235, 288, 289, 297, 393, 395, 408, 414, 447, 457, 460, 840, 841, 845, 981, 994, 1042, 1046], "compromis": [9, 171, 367, 381, 646, 678, 991, 1034], "risk": [9, 56, 200, 204, 218, 307, 347, 363, 380, 381, 395, 407, 412, 415, 566, 567, 885, 886, 888, 889, 890, 891, 984, 988, 1013, 1038], "flavanoid": [9, 378], "color_intens": 9, "363": [9, 13, 16, 1010], "plot_outlier_detection_win": [9, 16, 1010], "anomali": [9, 81, 128, 167, 214, 217, 228, 239, 244, 290, 291, 376, 407, 468, 511, 521, 562, 638, 676, 846, 861, 904, 994, 1008, 1010, 1037, 1038], "toi": [9, 21, 37, 40, 41, 42, 43, 48, 50, 53, 54, 56, 57, 58, 68, 69, 81, 87, 100, 115, 128, 133, 134, 167, 182, 190, 204, 214, 217, 228, 239, 244, 246, 290, 291, 301, 336, 346, 374, 387, 393, 415, 416, 439, 440, 441, 443, 445, 447, 448, 449, 451, 457, 468, 511, 513, 521, 562, 638, 676, 724, 754, 796, 846, 853, 861, 880, 904, 985, 987, 988, 994, 998, 1010, 1026], "lof": [9, 167, 214, 229, 239, 285, 298, 338, 562, 846, 904, 994, 1010], "discrimin": [9, 10, 15, 30, 36, 79, 80, 81, 82, 83, 104, 144, 146, 149, 167, 176, 215, 222, 237, 251, 293, 370, 378, 406, 415, 474, 511, 548, 549, 565, 630, 781, 849, 977, 985, 988, 991, 1002, 1004, 1010, 1011, 1022, 1026, 1034], "ellipsoid": [9, 30, 34, 36, 81, 82, 83, 167, 245, 246, 248, 249, 250, 251, 252, 253, 548, 549, 630, 795, 796, 982, 987, 1010], "mahalanobi": [9, 78, 82, 84, 103, 167, 203, 408, 410, 449, 456, 468, 469, 470, 471, 472, 473, 474, 475, 698, 776, 777, 778, 847, 982, 994, 1010, 1039, 1042, 1043], "distanc": [9, 40, 41, 56, 58, 61, 62, 66, 68, 78, 82, 84, 101, 103, 149, 164, 167, 173, 186, 201, 203, 221, 223, 226, 233, 239, 284, 286, 287, 292, 294, 296, 297, 306, 311, 316, 320, 321, 343, 352, 353, 367, 378, 390, 393, 395, 405, 408, 410, 413, 414, 418, 419, 439, 440, 441, 442, 443, 444, 445, 446, 448, 449, 451, 454, 455, 456, 457, 458, 460, 461, 462, 467, 468, 469, 470, 471, 472, 473, 474, 475, 537, 539, 546, 606, 607, 614, 618, 619, 621, 622, 627, 657, 658, 665, 667, 673, 674, 675, 676, 677, 687, 688, 689, 691, 692, 693, 694, 695, 698, 702, 723, 732, 756, 758, 760, 761, 762, 763, 766, 767, 768, 769, 770, 771, 776, 777, 778, 779, 790, 791, 794, 829, 840, 841, 842, 843, 844, 845, 846, 847, 848, 850, 851, 852, 853, 854, 855, 879, 894, 898, 900, 902, 904, 905, 906, 908, 909, 910, 911, 978, 982, 985, 986, 988, 994, 998, 1000, 1002, 1003, 1009, 1010, 1022, 1031, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048], "goal": [10, 18, 21, 53, 58, 87, 164, 171, 176, 200, 215, 218, 238, 239, 258, 259, 352, 368, 369, 376, 383, 386, 387, 395, 396, 397, 413, 417, 541, 547, 589, 592, 737, 988, 991, 993, 994, 1002, 1003, 1004, 1006, 1013, 1015, 1018, 1022, 1024, 1039], "boxplot": [10, 81, 126, 170, 173, 316], "gc": 10, "defaultdict": [10, 13, 20, 26, 173, 264, 353, 354], "make_regress": [10, 76, 179, 181, 182, 186, 203, 204, 274, 317, 323, 363, 377, 383, 384, 394, 553, 555, 559, 564, 605, 607, 608, 634, 645, 646, 648, 650, 652, 654, 662, 663, 664, 666, 670, 678, 858, 901, 988, 989, 991], "randomforestregressor": [10, 118, 131, 132, 135, 164, 165, 318, 323, 394, 415, 556, 557, 559, 561, 565, 567, 569, 631, 632, 910, 978, 1036, 1037, 1039, 1040, 1041, 1042, 1044, 1045, 1046, 1047, 1048], "sgdregressor": [10, 211, 367, 369, 393, 407, 645, 648, 666, 670, 675, 678, 858, 901, 906, 984, 1002, 1033, 1035, 1037, 1038, 1039, 1042, 1044, 1045, 1046, 1047], "shuffl": [10, 13, 15, 21, 22, 25, 28, 33, 49, 61, 62, 96, 118, 119, 121, 132, 165, 184, 208, 209, 214, 229, 262, 266, 267, 274, 284, 313, 326, 327, 328, 329, 341, 352, 353, 363, 376, 390, 395, 403, 468, 473, 487, 488, 490, 491, 494, 496, 502, 510, 511, 512, 513, 514, 518, 521, 523, 530, 536, 538, 539, 545, 546, 552, 553, 554, 555, 563, 564, 566, 567, 601, 604, 606, 657, 658, 665, 666, 667, 671, 673, 675, 676, 677, 686, 797, 799, 800, 801, 802, 803, 811, 814, 815, 816, 817, 818, 819, 821, 822, 823, 824, 825, 826, 827, 828, 830, 857, 858, 881, 900, 901, 902, 905, 936, 937, 957, 959, 983, 984, 989, 996, 1002, 1003, 1019, 1024, 1026, 1031, 1034, 1035, 1036, 1038, 1039, 1040, 1041, 1042], "atomic_benchmark_estim": 10, "verbos": [10, 43, 51, 55, 65, 71, 73, 75, 132, 187, 262, 302, 303, 306, 319, 330, 352, 379, 385, 395, 402, 439, 442, 446, 448, 451, 453, 458, 461, 463, 466, 470, 471, 477, 530, 535, 536, 537, 538, 539, 542, 544, 545, 546, 547, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 592, 593, 626, 631, 643, 644, 645, 646, 647, 649, 650, 651, 652, 653, 654, 655, 657, 658, 659, 660, 661, 662, 664, 665, 666, 667, 668, 675, 676, 677, 678, 679, 680, 681, 682, 683, 686, 689, 691, 693, 763, 776, 795, 796, 797, 800, 801, 803, 811, 819, 821, 822, 823, 824, 825, 827, 829, 831, 834, 849, 856, 857, 858, 859, 860, 861, 862, 897, 900, 901, 902, 903, 904, 905, 906, 954, 1009, 1033, 1034, 1036, 1038, 1039, 1040, 1042, 1043, 1045, 1046], "n_instanc": [10, 632], "zero": [10, 11, 14, 28, 35, 38, 41, 42, 53, 55, 80, 82, 83, 96, 99, 101, 114, 115, 124, 126, 127, 131, 146, 149, 165, 179, 184, 186, 191, 193, 194, 199, 200, 201, 204, 205, 206, 210, 215, 218, 233, 236, 237, 239, 246, 250, 252, 258, 261, 266, 270, 271, 289, 306, 317, 318, 343, 347, 353, 354, 357, 361, 367, 368, 376, 383, 387, 395, 406, 407, 408, 410, 413, 415, 416, 417, 418, 421, 445, 451, 468, 469, 470, 471, 472, 473, 474, 475, 476, 478, 479, 486, 496, 507, 508, 522, 525, 526, 530, 531, 534, 536, 537, 538, 539, 541, 542, 546, 547, 551, 556, 557, 558, 559, 560, 561, 563, 564, 565, 580, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 606, 607, 610, 635, 638, 642, 643, 644, 649, 657, 658, 663, 664, 665, 666, 667, 670, 675, 676, 677, 684, 685, 702, 708, 711, 712, 714, 715, 723, 727, 728, 732, 736, 738, 744, 781, 782, 783, 785, 794, 795, 812, 813, 816, 817, 829, 840, 841, 842, 843, 844, 846, 848, 849, 850, 851, 852, 853, 854, 855, 856, 863, 870, 872, 873, 875, 876, 877, 879, 880, 886, 887, 888, 889, 893, 900, 908, 909, 910, 911, 935, 963, 974, 978, 984, 986, 987, 990, 991, 992, 998, 999, 1000, 1002, 1003, 1004, 1015, 1022, 1024, 1031, 1033, 1035, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048], "atomic_benchmark": 10, "percentil": [10, 13, 72, 125, 239, 243, 306, 367, 591, 594, 595, 597, 598, 599, 602, 605, 631, 632, 746, 998, 1036, 1044], "bulk_benchmark_estim": 10, "n_bulk_repeat": 10, "whole": [10, 51, 75, 135, 165, 201, 258, 265, 339, 343, 357, 367, 379, 381, 393, 394, 409, 411, 415, 418, 448, 482, 537, 554, 555, 556, 557, 562, 563, 564, 565, 569, 684, 685, 797, 800, 801, 811, 824, 835, 836, 837, 838, 839, 977, 988, 998, 1024, 1039, 1041], "bulk_benchmark": 10, "benchmark_estim": 10, "repeat": [10, 34, 67, 80, 82, 124, 149, 152, 158, 159, 171, 176, 202, 256, 261, 262, 273, 279, 327, 353, 354, 383, 390, 395, 396, 405, 406, 407, 408, 413, 416, 417, 587, 592, 606, 607, 614, 626, 633, 658, 802, 812, 813, 816, 926, 978, 987, 1026, 1039, 1041], "atomic_runtim": 10, "bulk_runtim": 10, "pair": [10, 13, 38, 45, 59, 65, 101, 114, 116, 121, 125, 141, 156, 160, 180, 183, 192, 209, 212, 218, 233, 256, 261, 268, 270, 331, 335, 337, 339, 340, 341, 343, 345, 353, 358, 376, 393, 395, 405, 406, 409, 412, 416, 418, 436, 439, 440, 444, 449, 456, 462, 470, 471, 477, 492, 507, 508, 581, 591, 594, 595, 597, 598, 599, 609, 612, 613, 614, 616, 618, 619, 621, 622, 624, 626, 631, 632, 642, 691, 698, 699, 704, 705, 706, 714, 729, 738, 756, 757, 761, 764, 767, 768, 769, 770, 771, 772, 774, 776, 777, 778, 779, 780, 784, 828, 840, 841, 915, 988, 989, 990, 991, 998, 1004, 1031, 1043, 1044, 1046, 1047, 1048], "contain": [10, 13, 20, 25, 28, 34, 41, 50, 57, 59, 71, 72, 114, 116, 120, 128, 158, 170, 171, 173, 176, 191, 204, 218, 229, 236, 239, 240, 243, 251, 259, 261, 267, 270, 271, 282, 290, 291, 293, 306, 319, 327, 330, 352, 353, 374, 375, 376, 378, 381, 383, 385, 386, 387, 388, 389, 390, 394, 395, 402, 406, 407, 408, 409, 411, 412, 413, 415, 416, 417, 418, 420, 422, 432, 436, 437, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 461, 463, 464, 465, 467, 468, 469, 470, 471, 472, 473, 474, 475, 481, 482, 483, 484, 486, 487, 488, 489, 490, 491, 492, 493, 495, 499, 501, 502, 503, 507, 508, 509, 513, 514, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 546, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 580, 581, 582, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 609, 610, 611, 612, 613, 614, 615, 617, 618, 619, 620, 621, 622, 623, 624, 626, 627, 628, 629, 630, 631, 634, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 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273, 303, 312, 317, 318, 320, 321, 323, 328, 361, 369, 880, 998, 1040], "render": [12, 27, 54, 72, 73, 117, 119, 120, 128, 129, 132, 135, 146, 157, 170, 171, 172, 181, 195, 204, 230, 231, 232, 236, 240, 241, 242, 243, 251, 259, 261, 262, 268, 273, 303, 312, 317, 318, 320, 321, 323, 328, 361, 381, 383, 385, 386, 387, 395, 550, 588, 630, 699, 701, 877, 889, 912, 914, 987, 1004, 1044, 1048], "nbviewer": [12, 27, 72, 73, 117, 119, 120, 128, 129, 132, 135, 146, 157, 170, 171, 172, 181, 230, 231, 232, 236, 240, 241, 242, 243, 251, 259, 261, 262, 268, 273, 303, 312, 317, 318, 320, 321, 323, 328, 361], "nbsp": [12, 13, 27, 72, 73, 117, 119, 120, 125, 128, 129, 132, 135, 146, 157, 170, 171, 172, 181, 189, 230, 231, 232, 236, 240, 241, 242, 243, 251, 259, 261, 262, 268, 273, 303, 312, 317, 318, 320, 321, 323, 328, 361], "graphicallassocvifittedgraphicallassocv": 12, "group": [12, 38, 41, 56, 59, 61, 134, 171, 173, 200, 222, 236, 252, 256, 312, 323, 328, 353, 361, 376, 386, 395, 396, 399, 408, 414, 415, 416, 433, 445, 494, 587, 588, 590, 593, 717, 786, 797, 798, 799, 802, 803, 804, 805, 806, 807, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 821, 822, 823, 824, 825, 827, 873, 874, 910, 911, 984, 985, 987, 988, 989, 998, 1004, 1005, 1006, 1008, 1015, 1018, 1019, 1037, 1038, 1039, 1040, 1043, 1044, 1045, 1047, 1048], "amongst": [12, 1004], "indic": [12, 20, 21, 26, 27, 28, 49, 60, 61, 67, 76, 87, 91, 96, 112, 117, 123, 157, 160, 165, 169, 171, 197, 223, 233, 236, 255, 263, 264, 267, 268, 284, 297, 326, 327, 330, 352, 353, 354, 361, 368, 376, 381, 382, 383, 385, 389, 390, 393, 395, 404, 405, 406, 408, 410, 412, 413, 415, 416, 419, 423, 436, 439, 443, 449, 450, 451, 452, 454, 455, 456, 459, 463, 466, 471, 486, 507, 508, 510, 511, 512, 522, 531, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 580, 587, 588, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 606, 607, 619, 626, 627, 628, 629, 631, 632, 638, 646, 649, 650, 652, 653, 654, 660, 662, 664, 672, 674, 681, 682, 689, 691, 693, 695, 702, 706, 711, 712, 717, 718, 719, 723, 726, 727, 728, 729, 732, 736, 737, 738, 739, 752, 769, 776, 777, 778, 779, 781, 782, 783, 785, 786, 790, 791, 792, 794, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 827, 828, 829, 831, 833, 834, 840, 841, 842, 843, 844, 846, 848, 850, 851, 852, 864, 867, 871, 873, 881, 902, 903, 904, 905, 906, 908, 909, 910, 911, 912, 914, 916, 920, 921, 926, 941, 951, 952, 963, 964, 965, 966, 967, 968, 969, 978, 983, 984, 985, 988, 989, 990, 991, 992, 995, 996, 998, 999, 1002, 1003, 1009, 1015, 1019, 1022, 1024, 1031, 1032, 1033, 1034, 1035, 1036, 1039, 1041, 1042, 1044, 1046, 1047, 1048], "reflect": [12, 41, 47, 48, 81, 93, 97, 122, 171, 172, 200, 218, 270, 288, 353, 377, 380, 385, 395, 397, 405, 449, 455, 985, 988, 994, 996, 1023, 1032, 1040], "properti": [12, 26, 159, 160, 202, 217, 247, 286, 311, 361, 367, 370, 374, 381, 383, 398, 405, 406, 408, 410, 413, 415, 417, 418, 423, 441, 444, 450, 451, 452, 463, 464, 541, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 589, 590, 592, 593, 596, 609, 611, 612, 613, 614, 615, 617, 618, 619, 620, 621, 622, 623, 624, 631, 645, 651, 654, 659, 661, 673, 674, 698, 703, 704, 707, 797, 800, 801, 811, 828, 829, 859, 860, 873, 874, 875, 881, 900, 901, 902, 903, 904, 905, 906, 908, 909, 910, 911, 977, 980, 983, 984, 985, 988, 989, 991, 994, 998, 1001, 1003, 1004, 1005, 1009, 1015, 1036, 1040, 1041, 1044, 1045, 1048], "impact": [12, 13, 26, 37, 38, 43, 44, 60, 64, 65, 69, 76, 112, 145, 167, 169, 170, 171, 180, 202, 204, 218, 239, 255, 279, 287, 311, 321, 347, 353, 363, 367, 368, 374, 393, 396, 406, 408, 413, 446, 448, 535, 539, 546, 589, 665, 666, 667, 671, 673, 675, 676, 677, 691, 792, 797, 800, 801, 811, 823, 840, 841, 842, 877, 889, 900, 901, 923, 937, 959, 962, 977, 987, 995, 1003, 1010, 1038, 1041, 1044], "affinity_propag": [12, 45], "covariance_": [12, 35, 81, 83, 468, 469, 470, 471, 472, 473, 474, 475, 548, 549, 982, 1038], "n_label": [12, 93, 237, 395, 486, 522, 712, 719, 724, 737, 738, 752, 754, 790, 791, 817, 988, 1037, 1039], "lai": [12, 311, 343, 994], "canva": [12, 17], "manifold": [12, 40, 53, 68, 117, 224, 225, 226, 227, 233, 284, 293, 294, 316, 320, 321, 328, 370, 374, 395, 408, 413, 415, 501, 524, 529, 543, 548, 565, 849, 861, 870, 893, 923, 991, 1010, 1025, 1026, 1031, 1032, 1034, 1035, 1036, 1037, 1038, 1048], "dens": [12, 14, 56, 167, 178, 184, 194, 219, 320, 321, 330, 367, 390, 395, 408, 413, 415, 417, 442, 445, 446, 448, 463, 466, 495, 522, 523, 526, 533, 534, 543, 565, 584, 585, 606, 607, 628, 629, 651, 656, 657, 658, 665, 666, 667, 671, 672, 675, 676, 677, 686, 687, 688, 691, 692, 758, 759, 765, 777, 790, 857, 858, 865, 872, 873, 875, 877, 878, 879, 880, 889, 890, 891, 892, 893, 900, 901, 902, 903, 905, 906, 938, 959, 962, 978, 981, 984, 985, 988, 989, 991, 994, 998, 1000, 1001, 1002, 1003, 1004, 1010, 1031, 1033, 1037, 1038, 1039, 1040, 1041, 1042, 1044, 1045, 1046, 1047, 1048], "eigen_solv": [12, 45, 47, 67, 221, 222, 413, 451, 461, 534, 687, 688, 690, 692, 694, 985, 1032, 1041, 1044, 1045, 1046], "arpack": [12, 20, 45, 47, 67, 71, 222, 413, 450, 451, 452, 461, 534, 540, 543, 687, 688, 690, 692, 694, 985, 1037, 1038, 1043, 1047, 1048], "don": [12, 55, 71, 170, 222, 236, 237, 261, 284, 329, 336, 340, 352, 353, 367, 368, 375, 381, 383, 385, 387, 389, 393, 394, 399, 409, 416, 645, 651, 828, 908, 909, 910, 911, 952, 977, 984, 987, 989, 992, 994, 1002, 1012, 1024, 1035, 1040, 1047, 1048], "control": [12, 17, 35, 62, 100, 101, 118, 138, 157, 159, 184, 201, 204, 208, 232, 233, 262, 264, 279, 286, 303, 317, 319, 343, 357, 359, 360, 367, 368, 374, 377, 381, 383, 386, 389, 393, 395, 398, 402, 407, 408, 411, 412, 413, 414, 417, 418, 433, 439, 448, 453, 470, 471, 477, 530, 535, 536, 537, 538, 539, 542, 544, 545, 546, 547, 549, 550, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 592, 593, 595, 613, 618, 624, 626, 631, 633, 638, 640, 641, 645, 646, 648, 649, 650, 651, 653, 654, 655, 671, 673, 675, 676, 681, 682, 686, 691, 700, 708, 795, 796, 797, 799, 800, 801, 802, 803, 811, 812, 813, 814, 815, 816, 817, 819, 821, 822, 823, 824, 826, 827, 830, 831, 834, 857, 858, 879, 881, 892, 893, 900, 901, 902, 903, 905, 906, 908, 909, 910, 911, 914, 954, 956, 975, 977, 984, 985, 987, 988, 989, 991, 994, 995, 998, 1000, 1002, 1003, 1004, 1008, 1009, 1011, 1022, 1026, 1031, 1033, 1035, 1036, 1039, 1040, 1041, 1042, 1043, 1044, 1045], "neighbor": [12, 32, 40, 45, 48, 61, 67, 73, 128, 130, 133, 140, 164, 165, 222, 223, 225, 226, 229, 232, 238, 239, 255, 264, 276, 288, 289, 290, 291, 293, 294, 295, 297, 298, 302, 318, 320, 321, 352, 370, 373, 378, 390, 395, 398, 408, 414, 415, 416, 419, 422, 432, 440, 443, 444, 447, 449, 451, 456, 457, 460, 462, 495, 501, 503, 562, 569, 601, 606, 607, 626, 627, 630, 687, 688, 689, 690, 691, 692, 695, 779, 797, 826, 860, 861, 880, 896, 904, 962, 989, 994, 1009, 1010, 1011, 1018, 1023, 1025, 1026, 1031, 1033, 1034, 1035, 1036, 1037, 1038], "node": [12, 42, 122, 126, 184, 316, 322, 323, 357, 361, 389, 390, 408, 410, 413, 415, 440, 441, 444, 462, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 649, 653, 655, 681, 682, 691, 840, 841, 908, 909, 910, 911, 912, 914, 942, 956, 991, 993, 994, 995, 1004, 1034, 1035, 1036, 1037, 1038, 1040, 1043, 1044, 1045, 1046, 1048], "plane": [12, 149, 212, 234, 290, 341, 343, 344, 349, 378, 451, 461, 982, 984, 1003, 1022], "node_position_model": 12, "locallylinearembed": [12, 221, 222, 223, 320, 687, 689, 691, 985, 991, 1036, 1037, 1045, 1046, 1047], "n_neighbor": [12, 45, 63, 68, 133, 164, 221, 222, 223, 225, 229, 239, 284, 286, 287, 290, 291, 292, 293, 296, 311, 316, 318, 352, 415, 451, 601, 606, 607, 627, 687, 688, 690, 692, 695, 842, 843, 844, 846, 848, 849, 853, 895, 896, 978, 985, 991, 994, 1001, 1038, 1045, 1047, 1048], "output": [12, 15, 17, 26, 28, 38, 51, 54, 60, 99, 111, 112, 113, 115, 121, 132, 135, 136, 140, 141, 152, 160, 164, 165, 167, 170, 196, 228, 236, 240, 243, 244, 249, 255, 259, 261, 264, 268, 270, 271, 279, 284, 307, 310, 312, 313, 316, 319, 320, 322, 353, 354, 356, 357, 359, 361, 362, 374, 375, 376, 378, 381, 383, 387, 389, 393, 394, 395, 406, 407, 408, 409, 411, 412, 415, 416, 417, 418, 424, 429, 432, 436, 440, 441, 442, 444, 445, 446, 448, 449, 451, 458, 459, 461, 462, 463, 464, 465, 466, 467, 481, 482, 483, 484, 487, 490, 491, 492, 493, 494, 495, 496, 502, 504, 507, 508, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 547, 548, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 563, 564, 565, 566, 567, 568, 569, 580, 581, 582, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 610, 626, 627, 628, 629, 630, 631, 632, 634, 637, 638, 639, 640, 641, 645, 646, 649, 651, 652, 653, 656, 657, 659, 660, 661, 662, 665, 666, 667, 670, 672, 673, 675, 677, 678, 680, 681, 682, 683, 687, 688, 689, 691, 693, 695, 698, 699, 712, 713, 720, 721, 722, 724, 726, 733, 743, 744, 746, 748, 749, 750, 751, 752, 754, 759, 765, 770, 783, 786, 788, 789, 795, 796, 799, 800, 801, 802, 803, 809, 811, 812, 813, 814, 815, 816, 817, 821, 824, 825, 826, 828, 829, 830, 831, 832, 833, 834, 843, 844, 845, 849, 850, 852, 856, 857, 858, 859, 860, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 884, 888, 889, 892, 893, 897, 898, 900, 901, 902, 903, 904, 905, 906, 908, 909, 910, 911, 912, 913, 914, 915, 921, 923, 926, 937, 938, 944, 945, 947, 955, 975, 977, 978, 980, 982, 984, 985, 988, 989, 990, 991, 992, 995, 998, 1003, 1010, 1011, 1015, 1016, 1018, 1026, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048], "edg": [12, 27, 47, 67, 99, 122, 224, 268, 389, 395, 405, 408, 410, 452, 560, 561, 584, 585, 587, 588, 590, 795, 837, 842, 843, 844, 846, 848, 850, 851, 852, 853, 854, 865, 913, 998, 1001, 1013, 1037, 1039, 1042, 1045], "displai": [12, 13, 26, 28, 35, 38, 47, 49, 61, 72, 82, 83, 86, 87, 90, 96, 118, 128, 129, 132, 164, 167, 170, 171, 185, 187, 218, 228, 229, 239, 242, 243, 244, 250, 258, 262, 263, 268, 270, 271, 275, 294, 312, 317, 318, 338, 358, 367, 381, 383, 384, 388, 390, 398, 407, 409, 413, 416, 417, 422, 437, 463, 466, 467, 495, 540, 563, 629, 630, 631, 657, 686, 696, 697, 699, 700, 701, 712, 717, 780, 787, 797, 803, 811, 819, 826, 860, 861, 873, 875, 880, 898, 905, 912, 913, 914, 928, 954, 975, 988, 992, 1008, 1010, 1026, 1040, 1042, 1043, 1045, 1046], "strength": [12, 132, 159, 200, 204, 262, 316, 367, 411, 415, 445, 566, 567, 642, 647, 648, 651, 657, 658, 668, 671, 672, 673, 674, 675, 677, 679, 686, 781, 857, 858, 900, 901, 905, 906, 977, 984, 991, 994, 1002, 1003], "plan": [12, 375, 379, 381, 382, 396, 998, 1034], "fair": [12, 415, 689, 693, 829, 989, 1013], "crucial": [12, 169, 204, 256, 367, 380, 381, 396, 408, 418, 1003], "challeng": [12, 132, 152, 171, 179, 218, 229, 369, 376, 377, 408, 410, 461, 471, 988, 994, 1013, 1015], "overlap": [12, 13, 38, 56, 91, 126, 148, 224, 248, 353, 379, 405, 408, 412, 415, 416, 586, 790, 791, 798, 802, 804, 805, 806, 807, 815, 985, 998, 1037], "heurist": [12, 15, 124, 239, 352, 368, 408, 417, 448, 451, 460, 461, 507, 508, 536, 538, 545, 675, 676, 677, 690, 694, 800, 801, 902, 903, 904, 905, 906, 925, 985, 986, 991, 1002, 1004, 1013, 1022, 1031, 1032, 1036, 1040, 1043, 1044], "direct": [12, 86, 87, 96, 98, 104, 149, 206, 246, 252, 258, 293, 303, 311, 339, 344, 379, 381, 382, 385, 389, 396, 398, 408, 411, 413, 416, 417, 451, 461, 470, 471, 477, 533, 540, 548, 587, 588, 590, 601, 640, 687, 849, 982, 983, 984, 989, 991, 993, 1009, 1023, 1031, 1034], "nearest": [12, 14, 32, 33, 40, 41, 51, 52, 55, 59, 62, 67, 73, 79, 83, 90, 96, 99, 127, 128, 133, 140, 156, 165, 191, 212, 216, 223, 226, 232, 238, 255, 264, 276, 288, 290, 293, 294, 298, 302, 303, 311, 335, 339, 344, 345, 349, 370, 373, 378, 390, 395, 408, 414, 415, 419, 422, 432, 443, 445, 447, 449, 451, 456, 460, 461, 495, 501, 503, 606, 607, 626, 627, 629, 630, 634, 687, 688, 690, 691, 695, 790, 791, 797, 826, 840, 841, 842, 843, 844, 846, 847, 848, 849, 850, 851, 852, 853, 854, 860, 861, 865, 880, 962, 985, 994, 1003, 1009, 1010, 1011, 1018, 1026, 1031, 1033, 1034, 1035, 1036, 1038, 1040, 1041, 1043], "along": [12, 14, 57, 60, 67, 87, 119, 125, 187, 200, 234, 236, 265, 316, 353, 354, 363, 381, 385, 387, 389, 390, 395, 398, 399, 402, 405, 413, 415, 416, 417, 420, 532, 549, 586, 610, 611, 629, 645, 646, 649, 650, 651, 652, 653, 654, 655, 659, 660, 661, 662, 675, 677, 680, 681, 682, 683, 684, 685, 777, 778, 790, 797, 811, 869, 870, 873, 874, 877, 878, 879, 880, 885, 886, 887, 889, 890, 891, 916, 939, 963, 969, 977, 984, 991, 998, 1001, 1013, 1031, 1036, 1037, 1039, 1043, 1044], "linecollect": [12, 224, 232, 631], "facecolor": [12, 17, 27, 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"conclusion"], [189, "conclusion"], [279, "conclusion"], [287, "conclusion"], [313, "conclusion"], [343, "conclusion"]], "Out-of-core classification of text documents": [[8, "out-of-core-classification-of-text-documents"]], "Reuters Dataset related routines": [[8, "reuters-dataset-related-routines"]], "Main": [[8, "main"]], "Plot results": [[8, "plot-results"], [50, "plot-results"], [79, "plot-results"], [97, "plot-results"], [98, "plot-results"], [118, "plot-results"], [282, "plot-results"], [291, "plot-results"]], "Outlier detection on a real data set": [[9, "outlier-detection-on-a-real-data-set"]], "First example": [[9, "first-example"]], "Second example": [[9, "second-example"]], "Prediction Latency": [[10, "prediction-latency"], [367, "prediction-latency"]], "Benchmark and plot helper functions": [[10, "benchmark-and-plot-helper-functions"]], "Benchmark bulk/atomic prediction speed for various regressors": [[10, "benchmark-bulk-atomic-prediction-speed-for-various-regressors"]], "Benchmark n_features influence on prediction speed": [[10, "benchmark-n-features-influence-on-prediction-speed"]], "Benchmark throughput": [[10, "benchmark-throughput"]], "Species distribution modeling": [[11, "species-distribution-modeling"]], "Visualizing the stock market structure": [[12, "visualizing-the-stock-market-structure"]], "Retrieve the data from Internet": [[12, "retrieve-the-data-from-internet"]], "Learning a graph structure": [[12, "learning-a-graph-structure"]], "Clustering using affinity propagation": [[12, "clustering-using-affinity-propagation"]], "Embedding in 2D space": [[12, "embedding-in-2d-space"]], "Visualization": [[12, "visualization"], [339, "visualization"], [407, "visualization"]], "Lagged features for time series forecasting": [[13, "lagged-features-for-time-series-forecasting"]], "Analyzing the Bike Sharing Demand dataset": [[13, "analyzing-the-bike-sharing-demand-dataset"]], "Generating pandas-engineered lagged features": [[13, "generating-pandas-engineered-lagged-features"]], "Naive evaluation of the next hour bike demand regression": [[13, "naive-evaluation-of-the-next-hour-bike-demand-regression"]], "Proper next hour forecasting evaluation": [[13, "proper-next-hour-forecasting-evaluation"]], "Modeling predictive uncertainty via quantile regression": [[13, "modeling-predictive-uncertainty-via-quantile-regression"]], "A qualitative look at the predictions": [[13, "a-qualitative-look-at-the-predictions"]], "Compressive sensing: tomography reconstruction with L1 prior (Lasso)": [[14, "compressive-sensing-tomography-reconstruction-with-l1-prior-lasso"]], "Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation": [[15, "topic-extraction-with-non-negative-matrix-factorization-and-latent-dirichlet-allocation"]], "Computation times": [[16, "computation-times"], [23, "computation-times"], [29, "computation-times"], [36, "computation-times"], [69, "computation-times"], [77, "computation-times"], [84, "computation-times"], [88, "computation-times"], [94, "computation-times"], [107, "computation-times"], [109, "computation-times"], [136, "computation-times"], [142, "computation-times"], [150, "computation-times"], [162, "computation-times"], [166, "computation-times"], [174, "computation-times"], [177, "computation-times"], [219, "computation-times"], [227, "computation-times"], [244, "computation-times"], [253, "computation-times"], [277, "computation-times"], [280, "computation-times"], [283, "computation-times"], [298, "computation-times"], [304, "computation-times"], [314, "computation-times"], [324, "computation-times"], [332, "computation-times"], [333, "computation-times"], [350, "computation-times"], [355, "computation-times"], [362, "computation-times"], [1010, "computation-times"]], "Libsvm GUI": [[17, "libsvm-gui"]], "Wikipedia principal eigenvector": [[18, "wikipedia-principal-eigenvector"]], "Download data, if not already on disk": [[18, "download-data-if-not-already-on-disk"]], "Loading the redirect files": [[18, "loading-the-redirect-files"]], "Computing the Adjacency matrix": [[18, "computing-the-adjacency-matrix"]], "Computing Principal Singular Vector using Randomized SVD": [[18, "computing-principal-singular-vector-using-randomized-svd"]], "Computing Centrality scores": [[18, "computing-centrality-scores"]], "Biclustering": [[19, "biclustering"], [167, "biclustering"], [377, "biclustering"], [405, "biclustering"]], "Biclustering documents with the Spectral Co-clustering algorithm": [[20, "biclustering-documents-with-the-spectral-co-clustering-algorithm"]], "A demo of the Spectral Biclustering algorithm": [[21, "a-demo-of-the-spectral-biclustering-algorithm"]], "Generate sample data": [[21, "generate-sample-data"], [39, "generate-sample-data"], [56, "generate-sample-data"], [64, "generate-sample-data"], [79, "generate-sample-data"], [97, "generate-sample-data"], [98, "generate-sample-data"], [145, "generate-sample-data"], [235, "generate-sample-data"], [274, "generate-sample-data"], [296, "generate-sample-data"], [346, "generate-sample-data"]], "Fitting SpectralBiclustering": [[21, "fitting-spectralbiclustering"]], "Plotting results": [[21, "plotting-results"]], "A demo of the Spectral Co-Clustering algorithm": [[22, "a-demo-of-the-spectral-co-clustering-algorithm"]], "Calibration": [[24, "calibration"], [167, "calibration"]], "Probability calibration of classifiers": [[25, "probability-calibration-of-classifiers"]], "Generate synthetic dataset": [[25, "generate-synthetic-dataset"], [179, "generate-synthetic-dataset"], [179, "id1"], [184, "generate-synthetic-dataset"]], "Gaussian Naive-Bayes": [[25, "gaussian-naive-bayes"]], "Plot data and the predicted probabilities": [[25, "plot-data-and-the-predicted-probabilities"]], "Probability Calibration curves": [[26, "probability-calibration-curves"]], "Dataset": [[26, "dataset"], [28, "dataset"], [189, "dataset"], [267, "dataset"]], "Calibration curves": [[26, "calibration-curves"], [28, "calibration-curves"], [406, "calibration-curves"]], "Gaussian Naive Bayes": [[26, "gaussian-naive-bayes"], [990, "gaussian-naive-bayes"]], "Linear support vector classifier": [[26, "linear-support-vector-classifier"]], "Summary": [[26, "summary"], [123, "summary"], [354, "summary"]], "Probability Calibration for 3-class classification": [[27, "probability-calibration-for-3-class-classification"]], "Data": [[27, "data"]], "Fitting and calibration": [[27, "fitting-and-calibration"]], "Compare probabilities": [[27, "compare-probabilities"]], "Comparison of Calibration of Classifiers": [[28, "comparison-of-calibration-of-classifiers"]], "Classification": [[30, "classification"], [33, "classification"], [160, "classification"], [167, "classification"], [415, "classification"], [415, "id10"], [984, "classification"], [991, "id4"], [992, "classification"], [1002, "classification"], [1003, "classification"], [1004, "classification"], [1022, "classification"]], "Plot classification probability": [[31, "plot-classification-probability"]], "Classifier comparison": [[32, "classifier-comparison"]], "Recognizing hand-written digits": [[33, "recognizing-hand-written-digits"]], "Digits dataset": [[33, "digits-dataset"]], "Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification": [[34, "normal-ledoit-wolf-and-oas-linear-discriminant-analysis-for-classification"]], "Linear and Quadratic Discriminant Analysis with covariance ellipsoid": [[35, "linear-and-quadratic-discriminant-analysis-with-covariance-ellipsoid"]], "Data generation": [[35, "data-generation"], [50, "data-generation"], [58, "data-generation"], [128, "data-generation"], [148, "data-generation"], [158, "data-generation"], [251, "data-generation"], [326, "data-generation"], [347, "data-generation"]], "Plotting Functions": [[35, "plotting-functions"]], "Comparison of LDA and QDA": [[35, "comparison-of-lda-and-qda"]], "Clustering": [[37, "clustering"], [167, "clustering"], [408, "clustering"]], "Adjustment for chance in clustering performance evaluation": [[38, "adjustment-for-chance-in-clustering-performance-evaluation"]], "Defining the list of metrics to evaluate": [[38, "defining-the-list-of-metrics-to-evaluate"]], "First experiment: fixed ground truth labels and growing number of clusters": [[38, "first-experiment-fixed-ground-truth-labels-and-growing-number-of-clusters"]], "Second experiment: varying number of classes and clusters": [[38, "second-experiment-varying-number-of-classes-and-clusters"]], "Demo of affinity propagation clustering algorithm": [[39, "demo-of-affinity-propagation-clustering-algorithm"]], "Compute Affinity Propagation": [[39, "compute-affinity-propagation"]], "Plot result": [[39, "plot-result"], [64, "plot-result"], [68, "plot-result"], [68, "id2"]], "Agglomerative clustering with and without structure": [[40, "agglomerative-clustering-with-and-without-structure"]], "Agglomerative clustering with different metrics": [[41, "agglomerative-clustering-with-different-metrics"]], "Plot Hierarchical Clustering Dendrogram": [[42, "plot-hierarchical-clustering-dendrogram"]], "Compare BIRCH and MiniBatchKMeans": [[43, "compare-birch-and-minibatchkmeans"]], "Bisecting K-Means and Regular K-Means Performance Comparison": [[44, "bisecting-k-means-and-regular-k-means-performance-comparison"]], "Comparing different clustering algorithms on toy datasets": [[45, "comparing-different-clustering-algorithms-on-toy-datasets"]], "K-means Clustering": [[46, "k-means-clustering"]], "Segmenting the picture of greek coins in regions": [[47, "segmenting-the-picture-of-greek-coins-in-regions"]], "A demo of structured Ward hierarchical clustering on an image of coins": [[48, "a-demo-of-structured-ward-hierarchical-clustering-on-an-image-of-coins"]], "Generate data": [[48, "generate-data"], [68, "generate-data"], [81, "generate-data"], [194, "generate-data"], [303, "generate-data"]], "Define structure of the data": [[48, "define-structure-of-the-data"]], "Compute clustering": [[48, "compute-clustering"], [68, "compute-clustering"], [68, "id1"]], "Plot the results on an image": [[48, "plot-the-results-on-an-image"]], "Color Quantization using K-Means": [[49, "color-quantization-using-k-means"]], "Demo of DBSCAN clustering algorithm": [[50, "demo-of-dbscan-clustering-algorithm"]], "Compute DBSCAN": [[50, "compute-dbscan"]], "Online learning of a dictionary of parts of faces": [[51, "online-learning-of-a-dictionary-of-parts-of-faces"]], "Learn the dictionary of images": [[51, "learn-the-dictionary-of-images"]], "Plot the results": [[51, "plot-the-results"], [83, "plot-the-results"], [135, "plot-the-results"], [165, "plot-the-results"]], "Feature agglomeration": [[52, "feature-agglomeration"], [1005, "feature-agglomeration"], [1023, "feature-agglomeration"]], "Various Agglomerative Clustering on a 2D embedding of digits": [[53, "various-agglomerative-clustering-on-a-2d-embedding-of-digits"]], "Vector Quantization Example": [[54, "vector-quantization-example"]], "Original image": [[54, "original-image"]], "Compression via vector quantization": [[54, "compression-via-vector-quantization"]], "Encoding strategy": [[54, "encoding-strategy"]], "Memory footprint": [[54, "memory-footprint"]], "Feature agglomeration vs. univariate selection": [[55, "feature-agglomeration-vs-univariate-selection"]], "Demo of HDBSCAN clustering algorithm": [[56, "demo-of-hdbscan-clustering-algorithm"]], "Scale Invariance": [[56, "scale-invariance"]], "Multi-Scale Clustering": [[56, "multi-scale-clustering"]], "Hyperparameter Robustness": [[56, "hyperparameter-robustness"]], "min_cluster_size": [[56, "min-cluster-size"]], "min_samples": [[56, "min-samples"]], "dbscan_clustering": [[56, "dbscan-clustering"]], "Inductive Clustering": [[57, "inductive-clustering"]], "Demonstration of k-means assumptions": [[58, "demonstration-of-k-means-assumptions"]], "Fit models and plot results": [[58, "fit-models-and-plot-results"]], "Possible solutions": [[58, "possible-solutions"]], "Final remarks": [[58, "final-remarks"]], "A demo of K-Means clustering on the handwritten digits data": [[59, "a-demo-of-k-means-clustering-on-the-handwritten-digits-data"]], "Load the dataset": [[59, "load-the-dataset"]], "Define our evaluation benchmark": [[59, "define-our-evaluation-benchmark"]], "Run the benchmark": [[59, "run-the-benchmark"]], "Visualize the results on PCA-reduced data": [[59, "visualize-the-results-on-pca-reduced-data"]], "An example of K-Means++ initialization": [[60, "an-example-of-k-means-initialization"]], "Selecting the number of clusters with silhouette analysis on KMeans clustering": [[61, "selecting-the-number-of-clusters-with-silhouette-analysis-on-kmeans-clustering"]], "Empirical evaluation of the impact of k-means initialization": [[62, "empirical-evaluation-of-the-impact-of-k-means-initialization"]], "Comparing different hierarchical linkage methods on toy datasets": [[63, "comparing-different-hierarchical-linkage-methods-on-toy-datasets"]], "A demo of the mean-shift clustering algorithm": [[64, "a-demo-of-the-mean-shift-clustering-algorithm"]], "Compute clustering with MeanShift": [[64, "compute-clustering-with-meanshift"]], "Comparison of the K-Means and MiniBatchKMeans clustering algorithms": [[65, "comparison-of-the-k-means-and-minibatchkmeans-clustering-algorithms"]], "Generate the data": [[65, "generate-the-data"], [67, "generate-the-data"], [83, "generate-the-data"]], "Compute clustering with KMeans": [[65, "compute-clustering-with-kmeans"]], "Compute clustering with MiniBatchKMeans": [[65, "compute-clustering-with-minibatchkmeans"]], "Establishing parity between clusters": [[65, "establishing-parity-between-clusters"]], "Plotting the results": [[65, "plotting-the-results"], [113, "plotting-the-results"]], "Demo of OPTICS clustering algorithm": [[66, "demo-of-optics-clustering-algorithm"]], "Spectral clustering for image segmentation": [[67, "spectral-clustering-for-image-segmentation"]], "Plotting four circles": [[67, "plotting-four-circles"]], "Plotting two circles": [[67, "plotting-two-circles"]], "Hierarchical clustering: structured vs unstructured ward": [[68, "hierarchical-clustering-structured-vs-unstructured-ward"]], "We are defining k-Nearest Neighbors with 10 neighbors": [[68, "we-are-defining-k-nearest-neighbors-with-10-neighbors"]], "Pipelines and composite estimators": [[70, "pipelines-and-composite-estimators"], [167, "pipelines-and-composite-estimators"], [409, "pipelines-and-composite-estimators"]], "Column Transformer with Heterogeneous Data Sources": [[71, "column-transformer-with-heterogeneous-data-sources"]], "20 newsgroups dataset": [[71, "newsgroups-dataset"]], "Creating transformers": [[71, "creating-transformers"]], "Classification pipeline": [[71, "classification-pipeline"]], "Column Transformer with Mixed Types": [[72, "column-transformer-with-mixed-types"]], "Selecting dimensionality reduction with Pipeline and GridSearchCV": [[73, "selecting-dimensionality-reduction-with-pipeline-and-gridsearchcv"]], "Illustration of Pipeline and GridSearchCV": [[73, "illustration-of-pipeline-and-gridsearchcv"]], "Caching transformers within a Pipeline": [[73, "caching-transformers-within-a-pipeline"]], "Pipelining: chaining a PCA and a logistic regression": [[74, "pipelining-chaining-a-pca-and-a-logistic-regression"]], "Concatenating multiple feature extraction methods": [[75, "concatenating-multiple-feature-extraction-methods"]], "Effect of transforming the targets in regression model": [[76, "effect-of-transforming-the-targets-in-regression-model"]], "Synthetic example": [[76, "synthetic-example"]], "Real-world data set": [[76, "real-world-data-set"]], "Covariance estimation": [[78, "covariance-estimation"], [167, "covariance-estimation"], [410, "covariance-estimation"]], "Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood": [[79, "shrinkage-covariance-estimation-ledoitwolf-vs-oas-and-max-likelihood"]], "Compute the likelihood on test data": [[79, "compute-the-likelihood-on-test-data"]], "Compare different approaches to setting the regularization parameter": [[79, "compare-different-approaches-to-setting-the-regularization-parameter"]], "Ledoit-Wolf vs OAS estimation": [[80, "ledoit-wolf-vs-oas-estimation"]], "Robust covariance estimation and Mahalanobis distances relevance": [[81, "robust-covariance-estimation-and-mahalanobis-distances-relevance"]], "References:": [[81, null], [112, null], [172, null], [266, null], [267, null], [375, null], [376, null], [405, null], [405, null], [405, null], [406, null], [408, null], [408, null], [408, null], [408, null], [408, null], [408, null], [408, null], [408, null], [408, null], [408, null], [410, null], [410, null], [410, null], [410, null], [412, null], [412, 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Estimator": [[82, "minimum-covariance-determinant-estimator"]], "Evaluation": [[82, "evaluation"], [303, "evaluation"]], "Sparse inverse covariance estimation": [[83, "sparse-inverse-covariance-estimation"]], "Estimate the covariance": [[83, "estimate-the-covariance"]], "Cross decomposition": [[85, "cross-decomposition"], [167, "cross-decomposition"], [411, "cross-decomposition"]], "Compare cross decomposition methods": [[86, "compare-cross-decomposition-methods"]], "Dataset based latent variables model": [[86, "dataset-based-latent-variables-model"]], "Canonical (symmetric) PLS": [[86, "canonical-symmetric-pls"]], "Transform data": [[86, "transform-data"]], "Scatter plot of scores": [[86, "scatter-plot-of-scores"]], "PLS regression, with multivariate response, a.k.a. PLS2": [[86, "pls-regression-with-multivariate-response-a-k-a-pls2"]], "PLS regression, with univariate response, a.k.a. PLS1": [[86, "pls-regression-with-univariate-response-a-k-a-pls1"]], "CCA (PLS mode B with symmetric deflation)": [[86, "cca-pls-mode-b-with-symmetric-deflation"]], "Principal Component Regression vs Partial Least Squares Regression": [[87, "principal-component-regression-vs-partial-least-squares-regression"]], "The data": [[87, "the-data"]], "Projection on one component and predictive power": [[87, "projection-on-one-component-and-predictive-power"]], "Dataset examples": [[89, "dataset-examples"], [167, "dataset-examples"]], "The Digit Dataset": [[90, "the-digit-dataset"]], "The Iris Dataset": [[91, "the-iris-dataset"]], "Loading the iris dataset": [[91, "loading-the-iris-dataset"]], "Scatter Plot of the Iris dataset": [[91, "scatter-plot-of-the-iris-dataset"]], "Plot a PCA representation": [[91, "plot-a-pca-representation"]], "Plot randomly generated classification dataset": [[92, "plot-randomly-generated-classification-dataset"]], "Plot randomly generated multilabel dataset": [[93, "plot-randomly-generated-multilabel-dataset"]], "Decomposition": [[95, "decomposition"], [96, "decomposition"], [167, "decomposition"]], "Faces dataset decompositions": [[96, "faces-dataset-decompositions"]], "Dataset preparation": [[96, "dataset-preparation"], [221, "dataset-preparation"]], "Eigenfaces - PCA using randomized SVD": [[96, "eigenfaces-pca-using-randomized-svd"]], "Non-negative components - NMF": [[96, "non-negative-components-nmf"]], "Independent components - FastICA": [[96, "independent-components-fastica"]], "Sparse components - MiniBatchSparsePCA": [[96, "sparse-components-minibatchsparsepca"]], "Dictionary learning": [[96, "dictionary-learning"]], "Cluster centers - MiniBatchKMeans": [[96, "cluster-centers-minibatchkmeans"]], "Factor Analysis components - FA": [[96, "factor-analysis-components-fa"]], "Decomposition: Dictionary learning": [[96, "decomposition-dictionary-learning"]], "Dictionary learning - positive dictionary": [[96, "dictionary-learning-positive-dictionary"]], "Dictionary learning - positive code": [[96, "dictionary-learning-positive-code"]], "Dictionary learning - positive dictionary & code": [[96, "dictionary-learning-positive-dictionary-code"]], "Blind source separation using FastICA": [[97, "blind-source-separation-using-fastica"]], "Fit ICA and PCA models": [[97, "fit-ica-and-pca-models"]], "FastICA on 2D point clouds": [[98, "fastica-on-2d-point-clouds"]], "Image denoising using dictionary learning": [[99, "image-denoising-using-dictionary-learning"]], "Generate distorted image": [[99, "generate-distorted-image"]], "Display the distorted image": [[99, "display-the-distorted-image"]], "Extract reference patches": [[99, "extract-reference-patches"]], "Learn the dictionary from reference patches": [[99, "learn-the-dictionary-from-reference-patches"]], "Extract noisy patches and reconstruct them using the dictionary": [[99, "extract-noisy-patches-and-reconstruct-them-using-the-dictionary"]], "Incremental PCA": [[100, "incremental-pca"], [413, "incremental-pca"]], "Kernel PCA": [[101, "kernel-pca"]], "Projecting data: PCA vs. KernelPCA": [[101, "projecting-data-pca-vs-kernelpca"]], "Projecting into the original feature space": [[101, "projecting-into-the-original-feature-space"]], "PCA example with Iris Data-set": [[102, "pca-example-with-iris-data-set"]], "Model selection with Probabilistic PCA and Factor Analysis (FA)": [[103, "model-selection-with-probabilistic-pca-and-factor-analysis-fa"]], "Create the data": [[103, "create-the-data"]], "Fit the models": [[103, "fit-the-models"]], "Comparison of LDA and PCA 2D projection of Iris dataset": [[104, "comparison-of-lda-and-pca-2d-projection-of-iris-dataset"]], "Sparse coding with a precomputed dictionary": [[105, "sparse-coding-with-a-precomputed-dictionary"], [413, "sparse-coding-with-a-precomputed-dictionary"]], "Factor Analysis (with rotation) to visualize patterns": [[106, "factor-analysis-with-rotation-to-visualize-patterns"]], "Developing Estimators": [[108, "developing-estimators"], [167, "developing-estimators"]], "__sklearn_is_fitted__ as Developer API": [[110, "sklearn-is-fitted-as-developer-api"]], "An example custom estimator implementing a simple classifier": [[110, "an-example-custom-estimator-implementing-a-simple-classifier"]], "Ensemble methods": [[111, "ensemble-methods"], [167, "ensemble-methods"]], "Multi-class AdaBoosted Decision Trees": [[112, "multi-class-adaboosted-decision-trees"]], "Creating the dataset": [[112, "creating-the-dataset"]], "Training the AdaBoostClassifier": [[112, "training-the-adaboostclassifier"]], "Analysis": [[112, "analysis"]], "Convergence of the AdaBoostClassifier": [[112, "convergence-of-the-adaboostclassifier"]], "Errors and weights of the Weak Learners": [[112, "errors-and-weights-of-the-weak-learners"]], "Decision Tree Regression with AdaBoost": [[113, "decision-tree-regression-with-adaboost"]], "Preparing the data": [[113, "preparing-the-data"], [176, "preparing-the-data"]], "Training and prediction with DecisionTree and AdaBoost Regressors": [[113, "training-and-prediction-with-decisiontree-and-adaboost-regressors"]], "Two-class AdaBoost": [[114, "two-class-adaboost"]], "Single estimator versus bagging: bias-variance decomposition": [[115, "single-estimator-versus-bagging-bias-variance-decomposition"]], "OOB Errors for Random Forests": [[116, "oob-errors-for-random-forests"]], "Feature transformations with ensembles of trees": [[117, "feature-transformations-with-ensembles-of-trees"]], "Comparing Random Forests and Histogram Gradient Boosting models": [[118, "comparing-random-forests-and-histogram-gradient-boosting-models"]], "Load dataset": [[118, "load-dataset"]], "Compute score and computation times": [[118, "compute-score-and-computation-times"]], "Feature importances with a forest of trees": [[119, "feature-importances-with-a-forest-of-trees"]], "Data generation and model fitting": [[119, "data-generation-and-model-fitting"]], "Feature importance based on mean decrease in impurity": [[119, "feature-importance-based-on-mean-decrease-in-impurity"]], "Feature importance based on feature permutation": [[119, "feature-importance-based-on-feature-permutation"]], "Pixel importances with a parallel forest of trees": [[120, "pixel-importances-with-a-parallel-forest-of-trees"]], "Loading the data and model fitting": [[120, "loading-the-data-and-model-fitting"]], "Feature importance based on mean decrease in impurity (MDI)": [[120, "feature-importance-based-on-mean-decrease-in-impurity-mdi"]], "Plot the decision surfaces of ensembles of trees on the iris dataset": [[121, "plot-the-decision-surfaces-of-ensembles-of-trees-on-the-iris-dataset"]], "Categorical Feature Support in Gradient Boosting": [[122, "categorical-feature-support-in-gradient-boosting"]], "Load Ames Housing dataset": [[122, "load-ames-housing-dataset"]], "Gradient boosting estimator with dropped categorical features": [[122, "gradient-boosting-estimator-with-dropped-categorical-features"]], "Gradient boosting estimator with one-hot encoding": [[122, "gradient-boosting-estimator-with-one-hot-encoding"]], "Gradient boosting estimator with ordinal encoding": [[122, "gradient-boosting-estimator-with-ordinal-encoding"]], "Gradient boosting estimator with native categorical support": [[122, "gradient-boosting-estimator-with-native-categorical-support"]], "Model comparison": [[122, "model-comparison"]], "Limiting the number of splits": [[122, "limiting-the-number-of-splits"]], "Early stopping in Gradient Boosting": [[123, "early-stopping-in-gradient-boosting"]], "Data Preparation": [[123, "data-preparation"]], "Model Training and Comparison": [[123, "model-training-and-comparison"]], "Error Calculation": [[123, "error-calculation"]], "Visualize Comparison": [[123, "visualize-comparison"]], "Gradient Boosting Out-of-Bag estimates": [[124, "gradient-boosting-out-of-bag-estimates"]], "Prediction Intervals for Gradient Boosting Regression": [[125, "prediction-intervals-for-gradient-boosting-regression"]], "Fitting non-linear quantile and least squares regressors": [[125, "fitting-non-linear-quantile-and-least-squares-regressors"]], "Analysis of the error metrics": [[125, "analysis-of-the-error-metrics"]], "Calibration of the confidence interval": [[125, "calibration-of-the-confidence-interval"]], "Tuning the hyper-parameters of the quantile regressors": [[125, "tuning-the-hyper-parameters-of-the-quantile-regressors"]], "Gradient Boosting regression": [[126, "gradient-boosting-regression"]], "Data preprocessing": [[126, "data-preprocessing"]], "Fit regression model": [[126, "fit-regression-model"], [296, "fit-regression-model"], [346, "fit-regression-model"]], "Plot training deviance": [[126, "plot-training-deviance"]], "Plot feature importance": [[126, "plot-feature-importance"]], "Gradient Boosting regularization": [[127, "gradient-boosting-regularization"]], "IsolationForest example": [[128, "isolationforest-example"]], "Training of the model": [[128, "training-of-the-model"]], "Plot discrete decision boundary": [[128, "plot-discrete-decision-boundary"]], "Plot path length decision boundary": [[128, "plot-path-length-decision-boundary"]], "Monotonic Constraints": [[129, "monotonic-constraints"], [415, "monotonic-constraints"]], "Using feature names to specify monotonic constraints": [[129, "using-feature-names-to-specify-monotonic-constraints"]], "Hashing feature transformation using Totally Random Trees": [[130, "hashing-feature-transformation-using-totally-random-trees"]], "Comparing random forests and the multi-output meta estimator": [[131, "comparing-random-forests-and-the-multi-output-meta-estimator"]], "Combine predictors using stacking": [[132, "combine-predictors-using-stacking"]], "Download the dataset": [[132, "download-the-dataset"]], "Make pipeline to preprocess the data": [[132, "make-pipeline-to-preprocess-the-data"]], "Stack of predictors on a single data set": [[132, "stack-of-predictors-on-a-single-data-set"]], "Measure and plot the results": [[132, "measure-and-plot-the-results"]], "Plot the decision boundaries of a VotingClassifier": [[133, "plot-the-decision-boundaries-of-a-votingclassifier"]], "Plot class probabilities calculated by the VotingClassifier": [[134, "plot-class-probabilities-calculated-by-the-votingclassifier"]], "Plot individual and voting regression predictions": [[135, "plot-individual-and-voting-regression-predictions"]], "Training classifiers": [[135, "training-classifiers"]], "Making predictions": [[135, "making-predictions"]], "Tutorial exercises": [[137, "tutorial-exercises"], [167, "tutorial-exercises"]], "Cross-validation on diabetes Dataset Exercise": [[138, "cross-validation-on-diabetes-dataset-exercise"]], "Load dataset and apply GridSearchCV": [[138, "load-dataset-and-apply-gridsearchcv"]], "Plot error lines showing +/- std. errors of the scores": [[138, "plot-error-lines-showing-std-errors-of-the-scores"]], "Bonus: how much can you trust the selection of alpha?": [[138, "bonus-how-much-can-you-trust-the-selection-of-alpha"]], "Cross-validation on Digits Dataset Exercise": [[139, "cross-validation-on-digits-dataset-exercise"]], "Digits Classification Exercise": [[140, "digits-classification-exercise"]], "SVM Exercise": [[141, "svm-exercise"]], "Feature Selection": [[143, "feature-selection"], [167, "feature-selection"]], "Comparison of F-test and mutual information": [[144, "comparison-of-f-test-and-mutual-information"]], "Univariate Feature Selection": [[145, "univariate-feature-selection"]], "Univariate feature selection": [[145, "id1"], [417, "univariate-feature-selection"]], "Compare with SVMs": [[145, "compare-with-svms"]], "Pipeline ANOVA SVM": [[146, "pipeline-anova-svm"]], "Recursive feature elimination": [[147, "recursive-feature-elimination"], [417, "recursive-feature-elimination"]], "Recursive feature elimination with cross-validation": [[148, "recursive-feature-elimination-with-cross-validation"]], "Model training and selection": [[148, "model-training-and-selection"], [251, "model-training-and-selection"]], "Plot number of features VS. cross-validation scores": [[148, "plot-number-of-features-vs-cross-validation-scores"]], "Model-based and sequential feature selection": [[149, "model-based-and-sequential-feature-selection"]], "Loading the data": [[149, "loading-the-data"]], "Feature importance from coefficients": [[149, "feature-importance-from-coefficients"]], "Selecting features based on importance": [[149, "selecting-features-based-on-importance"]], "Selecting features with Sequential Feature Selection": [[149, "selecting-features-with-sequential-feature-selection"]], "Using negative tolerance values": [[149, "using-negative-tolerance-values"]], "Gaussian Process for Machine Learning": [[151, "gaussian-process-for-machine-learning"], [167, "gaussian-process-for-machine-learning"]], "Comparison of kernel ridge and Gaussian process regression": [[152, "comparison-of-kernel-ridge-and-gaussian-process-regression"]], "Generating a dataset": [[152, "generating-a-dataset"]], "Limitations of a simple linear model": [[152, "limitations-of-a-simple-linear-model"]], "Kernel methods: kernel ridge and Gaussian process": [[152, "kernel-methods-kernel-ridge-and-gaussian-process"]], "Kernel ridge": [[152, "kernel-ridge"]], "Gaussian process regression": [[152, "gaussian-process-regression"]], "Final conclusion": [[152, "final-conclusion"]], "Probabilistic predictions with Gaussian process classification (GPC)": [[153, "probabilistic-predictions-with-gaussian-process-classification-gpc"]], "Gaussian process classification (GPC) on iris dataset": [[154, "gaussian-process-classification-gpc-on-iris-dataset"], [418, "gaussian-process-classification-gpc-on-iris-dataset"]], "Iso-probability lines for Gaussian Processes classification (GPC)": [[155, "iso-probability-lines-for-gaussian-processes-classification-gpc"]], "Illustration of Gaussian process classification (GPC) on the XOR dataset": [[156, "illustration-of-gaussian-process-classification-gpc-on-the-xor-dataset"]], "Forecasting of CO2 level on Mona Loa dataset using Gaussian process regression (GPR)": [[157, "forecasting-of-co2-level-on-mona-loa-dataset-using-gaussian-process-regression-gpr"]], "Build the dataset": [[157, "build-the-dataset"]], "Design the proper kernel": [[157, "design-the-proper-kernel"]], "Model fitting and extrapolation": [[157, "model-fitting-and-extrapolation"]], "Interpretation of kernel hyperparameters": [[157, "interpretation-of-kernel-hyperparameters"]], "Ability of Gaussian process regression (GPR) to estimate data noise-level": [[158, "ability-of-gaussian-process-regression-gpr-to-estimate-data-noise-level"]], "Optimisation of kernel hyperparameters in GPR": [[158, "optimisation-of-kernel-hyperparameters-in-gpr"]], "Gaussian Processes regression: basic introductory example": [[159, "gaussian-processes-regression-basic-introductory-example"]], "Dataset generation": [[159, "dataset-generation"], [202, "dataset-generation"]], "Example with noise-free target": [[159, "example-with-noise-free-target"]], "Example with noisy targets": [[159, "example-with-noisy-targets"]], "Gaussian processes on discrete data structures": [[160, "gaussian-processes-on-discrete-data-structures"]], "Sequence similarity matrix under the kernel": [[160, "sequence-similarity-matrix-under-the-kernel"]], "Regression": [[160, "regression"], [415, "regression"], [415, "id9"], [984, "regression"], [992, "regression"], [1002, "regression"], [1003, "regression"], [1004, "regression"]], "Illustration of prior and posterior Gaussian process for different kernels": [[161, "illustration-of-prior-and-posterior-gaussian-process-for-different-kernels"]], "Helper function": [[161, "helper-function"]], "Dataset and Gaussian process generation": [[161, "dataset-and-gaussian-process-generation"]], "Kernel cookbook": [[161, "kernel-cookbook"]], "Radial Basis Function kernel": [[161, "radial-basis-function-kernel"]], "Rational Quadradtic kernel": [[161, "rational-quadradtic-kernel"]], "Exp-Sine-Squared kernel": [[161, "exp-sine-squared-kernel"], [418, "exp-sine-squared-kernel"]], "Dot-product kernel": [[161, "dot-product-kernel"]], "Mat\u00e9rn kernel": [[161, "matern-kernel"], [418, "matern-kernel"]], "Missing Value Imputation": [[163, "missing-value-imputation"], [167, "missing-value-imputation"]], "Imputing missing values with variants of IterativeImputer": [[164, "imputing-missing-values-with-variants-of-iterativeimputer"]], "Imputing missing values before building an estimator": [[165, "imputing-missing-values-before-building-an-estimator"]], "Download the data and make missing values sets": [[165, "download-the-data-and-make-missing-values-sets"]], "Impute the missing data and score": [[165, "impute-the-missing-data-and-score"]], "Missing information": [[165, "missing-information"]], "Estimate the score": [[165, "estimate-the-score"]], "Replace missing values by 0": [[165, "replace-missing-values-by-0"]], "kNN-imputation of the missing values": [[165, "knn-imputation-of-the-missing-values"]], "Impute missing values with mean": [[165, "impute-missing-values-with-mean"]], "Iterative imputation of the missing values": [[165, "iterative-imputation-of-the-missing-values"]], "Examples": [[167, "examples"], [369, "examples"], [376, null], [412, null], [412, null], [417, null], [417, null], [418, null], [984, null], [984, null], [984, null], [1001, null], [1001, null], [1005, null], [1005, null], [1005, null], [1031, "examples"]], "Release Highlights": [[167, "release-highlights"], [315, "release-highlights"]], "Decision Trees": [[167, "decision-trees"], [356, "decision-trees"], [1004, "decision-trees"]], "Gaussian Mixture Models": [[167, "gaussian-mixture-models"], [245, "gaussian-mixture-models"]], "Generalized Linear Models": [[167, "generalized-linear-models"], [178, "generalized-linear-models"], [984, "generalized-linear-models"]], "Inspection": [[167, "inspection"], [168, "inspection"], [397, "inspection"]], "Kernel Approximation": [[167, "kernel-approximation"], [175, "kernel-approximation"], [980, "kernel-approximation"]], "Manifold learning": [[167, "manifold-learning"], [220, "manifold-learning"], [985, "manifold-learning"]], "Miscellaneous": [[167, "miscellaneous"], [228, "miscellaneous"], [407, "miscellaneous"], [1039, "miscellaneous"], [1039, "id40"], [1040, "miscellaneous"], [1041, "miscellaneous"], [1042, "miscellaneous"], [1042, "id13"], [1043, "miscellaneous"], [1044, "miscellaneous"], [1047, "miscellaneous"]], "Model Selection": [[167, "model-selection"], [254, "model-selection"]], "Multiclass methods": [[167, "multiclass-methods"], [278, "multiclass-methods"]], "Multioutput methods": [[167, "multioutput-methods"], [281, "multioutput-methods"]], "Nearest Neighbors": [[167, "nearest-neighbors"], [285, "nearest-neighbors"], [991, "nearest-neighbors"]], "Neural Networks": [[167, "neural-networks"], [299, "neural-networks"]], "Preprocessing": [[167, "preprocessing"], [305, "preprocessing"]], "Semi Supervised Classification": [[167, "semi-supervised-classification"], [325, "semi-supervised-classification"]], "Support Vector Machines": [[167, "support-vector-machines"], [334, "support-vector-machines"], [1003, "support-vector-machines"]], "Working with text documents": [[167, "working-with-text-documents"], [351, "working-with-text-documents"]], "Failure of Machine Learning to infer causal effects": [[169, "failure-of-machine-learning-to-infer-causal-effects"]], "The dataset: simulated hourly wages": [[169, "the-dataset-simulated-hourly-wages"]], "Description of the simulated data": [[169, "description-of-the-simulated-data"]], "Income prediction with fully observed variables": [[169, "income-prediction-with-fully-observed-variables"]], "Income prediction with partial observations": [[169, "income-prediction-with-partial-observations"]], "Lessons learned": [[169, "lessons-learned"], [170, "lessons-learned"]], "Common pitfalls in the interpretation of coefficients of linear models": [[170, "common-pitfalls-in-the-interpretation-of-coefficients-of-linear-models"]], "The dataset: wages": [[170, "the-dataset-wages"]], "The machine-learning pipeline": [[170, "the-machine-learning-pipeline"]], "Processing the dataset": [[170, "processing-the-dataset"]], "Interpreting coefficients: scale matters": [[170, "interpreting-coefficients-scale-matters"]], "Checking the variability of the coefficients": [[170, "checking-the-variability-of-the-coefficients"]], "The problem of correlated variables": [[170, "the-problem-of-correlated-variables"]], "Preprocessing numerical variables": [[170, "preprocessing-numerical-variables"]], "Linear models with regularization": [[170, "linear-models-with-regularization"]], "Linear models with sparse coefficients": [[170, "linear-models-with-sparse-coefficients"]], "Wrong causal interpretation": [[170, "wrong-causal-interpretation"]], "Partial Dependence and Individual Conditional Expectation Plots": [[171, "partial-dependence-and-individual-conditional-expectation-plots"]], "Bike sharing dataset preprocessing": [[171, "bike-sharing-dataset-preprocessing"]], "Preprocessor for machine-learning models": [[171, "preprocessor-for-machine-learning-models"]], "Preprocessor for the neural network model": [[171, "preprocessor-for-the-neural-network-model"]], "Preprocessor for the gradient boosting model": [[171, "preprocessor-for-the-gradient-boosting-model"]], "1-way partial dependence with different models": [[171, "way-partial-dependence-with-different-models"]], "Multi-layer perceptron": [[171, "multi-layer-perceptron"]], "Gradient boosting": [[171, "gradient-boosting"]], "Analysis of the plots": [[171, "analysis-of-the-plots"]], "ICE vs. PDP": [[171, "ice-vs-pdp"]], "2D interaction plots": [[171, "d-interaction-plots"]], "3D representation": [[171, "d-representation"]], "Permutation Importance vs Random Forest Feature Importance (MDI)": [[172, "permutation-importance-vs-random-forest-feature-importance-mdi"]], "Data Loading and Feature Engineering": [[172, "data-loading-and-feature-engineering"]], "Accuracy of the Model": [[172, "accuracy-of-the-model"]], "Tree\u2019s Feature Importance from Mean Decrease in Impurity (MDI)": [[172, "tree-s-feature-importance-from-mean-decrease-in-impurity-mdi"]], "Permutation Importance with Multicollinear or Correlated Features": [[173, "permutation-importance-with-multicollinear-or-correlated-features"]], "Random Forest Feature Importance on Breast Cancer Data": [[173, "random-forest-feature-importance-on-breast-cancer-data"]], "Handling Multicollinear Features": [[173, "handling-multicollinear-features"]], "Scalable learning with polynomial kernel approximation": [[176, "scalable-learning-with-polynomial-kernel-approximation"]], "Partitioning the data": [[176, "partitioning-the-data"]], "Feature normalization": [[176, "feature-normalization"]], "Establishing a baseline model": [[176, "establishing-a-baseline-model"]], "Establishing the kernel approximation model": [[176, "establishing-the-kernel-approximation-model"]], "Establishing the kernelized SVM model": [[176, "establishing-the-kernelized-svm-model"]], "Comparing the results": [[176, "comparing-the-results"]], "Comparing Linear Bayesian Regressors": [[179, "comparing-linear-bayesian-regressors"]], "Models robustness to recover the ground truth weights": [[179, "models-robustness-to-recover-the-ground-truth-weights"]], "Fit the regressors": [[179, "fit-the-regressors"], [179, "id2"]], "Plot the true and estimated coefficients": [[179, "plot-the-true-and-estimated-coefficients"]], "Plot the marginal log-likelihood": [[179, "plot-the-marginal-log-likelihood"]], "Bayesian regressions with polynomial feature expansion": [[179, "bayesian-regressions-with-polynomial-feature-expansion"]], "Plotting polynomial regressions with std errors of the scores": [[179, "plotting-polynomial-regressions-with-std-errors-of-the-scores"]], "Curve Fitting with Bayesian Ridge Regression": [[180, "curve-fitting-with-bayesian-ridge-regression"]], "Generate sinusoidal data with noise": [[180, "generate-sinusoidal-data-with-noise"]], "Fit by cubic polynomial": [[180, "fit-by-cubic-polynomial"]], "Plot the true and predicted curves with log marginal likelihood (L)": [[180, "plot-the-true-and-predicted-curves-with-log-marginal-likelihood-l"]], "Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples": [[181, "fitting-an-elastic-net-with-a-precomputed-gram-matrix-and-weighted-samples"]], "HuberRegressor vs Ridge on dataset with strong outliers": [[182, "huberregressor-vs-ridge-on-dataset-with-strong-outliers"]], "Logistic Regression 3-class Classifier": [[183, "logistic-regression-3-class-classifier"]], "L1-based models for Sparse Signals": [[184, "l1-based-models-for-sparse-signals"]], "Lasso": [[184, "lasso"], [984, "lasso"]], "Automatic Relevance Determination (ARD)": [[184, "automatic-relevance-determination-ard"]], "ElasticNet": [[184, "elasticnet"]], "Plot and analysis of the results": [[184, "plot-and-analysis-of-the-results"]], "Conclusions": [[184, "conclusions"]], "Lasso and Elastic Net": [[185, "lasso-and-elastic-net"]], "Lasso on dense and sparse data": [[186, "lasso-on-dense-and-sparse-data"]], "Comparing the two Lasso implementations on Dense data": [[186, "comparing-the-two-lasso-implementations-on-dense-data"]], "Comparing the two Lasso implementations on Sparse data": [[186, "comparing-the-two-lasso-implementations-on-sparse-data"]], "Lasso path using LARS": [[187, "lasso-path-using-lars"]], "Lasso model selection via information criteria": [[188, "lasso-model-selection-via-information-criteria"]], "Lasso model selection: AIC-BIC / cross-validation": [[189, "lasso-model-selection-aic-bic-cross-validation"]], "Selecting Lasso via an information criterion": [[189, "selecting-lasso-via-an-information-criterion"]], "Selecting Lasso via cross-validation": [[189, "selecting-lasso-via-cross-validation"]], "Lasso via coordinate descent": [[189, "lasso-via-coordinate-descent"]], "Lasso via least angle regression": [[189, "lasso-via-least-angle-regression"]], "Summary of cross-validation approach": [[189, "summary-of-cross-validation-approach"]], "Logistic function": [[190, "logistic-function"]], "L1 Penalty and Sparsity in Logistic Regression": [[191, "l1-penalty-and-sparsity-in-logistic-regression"]], "Plot multinomial and One-vs-Rest Logistic Regression": [[192, "plot-multinomial-and-one-vs-rest-logistic-regression"]], "Regularization path of L1- Logistic Regression": [[193, "regularization-path-of-l1-logistic-regression"]], "Load data": [[193, "load-data"]], "Compute regularization path": [[193, "compute-regularization-path"]], "Plot regularization path": [[193, "plot-regularization-path"]], "Joint feature selection with multi-task Lasso": [[194, "joint-feature-selection-with-multi-task-lasso"]], "Fit models": [[194, "fit-models"], [282, "fit-models"]], "Plot support and time series": [[194, "plot-support-and-time-series"]], "Non-negative least squares": [[195, "non-negative-least-squares"]], "Linear Regression Example": [[196, "linear-regression-example"]], "Sparsity Example: Fitting only features 1  and 2": [[197, "sparsity-example-fitting-only-features-1-and-2"]], "Ordinary Least Squares and Ridge Regression Variance": [[198, "ordinary-least-squares-and-ridge-regression-variance"]], "Orthogonal Matching Pursuit": [[199, "orthogonal-matching-pursuit"]], "Poisson regression and non-normal loss": [[200, "poisson-regression-and-non-normal-loss"]], "The French Motor Third-Party Liability Claims dataset": [[200, "the-french-motor-third-party-liability-claims-dataset"]], "A constant prediction baseline": [[200, "a-constant-prediction-baseline"]], "(Generalized) linear models": [[200, "generalized-linear-models"]], "Gradient Boosting Regression Trees for Poisson regression": [[200, "gradient-boosting-regression-trees-for-poisson-regression"]], "Evaluation of the calibration of predictions": [[200, "evaluation-of-the-calibration-of-predictions"]], "Evaluation of the ranking power": [[200, "evaluation-of-the-ranking-power"]], "Main takeaways": [[200, "main-takeaways"]], "Polynomial and Spline interpolation": [[201, "polynomial-and-spline-interpolation"]], "Periodic Splines": [[201, "periodic-splines"]], "Quantile regression": [[202, "quantile-regression"]], "Fitting a QuantileRegressor": [[202, "fitting-a-quantileregressor"]], "Comparing QuantileRegressor and LinearRegression": [[202, "comparing-quantileregressor-and-linearregression"]], "Robust linear model estimation using RANSAC": [[203, "robust-linear-model-estimation-using-ransac"]], "Ridge coefficients as a function of the L2 Regularization": [[204, "ridge-coefficients-as-a-function-of-the-l2-regularization"]], "Purpose of this example": [[204, "purpose-of-this-example"]], "Creating a non-noisy data set": [[204, "creating-a-non-noisy-data-set"]], "Training the Ridge Regressor": [[204, "training-the-ridge-regressor"]], "Plotting trained Coefficients and Mean Squared Errors": [[204, "plotting-trained-coefficients-and-mean-squared-errors"]], "Interpreting the plots": [[204, "interpreting-the-plots"]], "Plot Ridge coefficients as a function of the regularization": [[205, "plot-ridge-coefficients-as-a-function-of-the-regularization"]], "Compute paths": [[205, "compute-paths"]], "Display results": [[205, "display-results"]], "Robust linear estimator fitting": [[206, "robust-linear-estimator-fitting"]], "Comparing various online solvers": [[207, "comparing-various-online-solvers"]], "Early stopping of Stochastic Gradient Descent": [[208, "early-stopping-of-stochastic-gradient-descent"]], "Plot multi-class SGD on the iris dataset": [[209, "plot-multi-class-sgd-on-the-iris-dataset"]], "SGD: convex loss functions": [[210, "sgd-convex-loss-functions"]], "SGD: Penalties": [[211, "sgd-penalties"]], "SGD: Maximum margin separating hyperplane": [[212, "sgd-maximum-margin-separating-hyperplane"]], "SGD: Weighted samples": [[213, "sgd-weighted-samples"]], "One-Class SVM versus One-Class SVM using Stochastic Gradient Descent": [[214, "one-class-svm-versus-one-class-svm-using-stochastic-gradient-descent"]], "Multiclass sparse logistic regression on 20newgroups": [[215, "multiclass-sparse-logistic-regression-on-20newgroups"]], "MNIST classification using multinomial logistic + L1": [[216, "mnist-classification-using-multinomial-logistic-l1"]], "Theil-Sen Regression": [[217, "theil-sen-regression"]], "Outliers only in the y direction": [[217, "outliers-only-in-the-y-direction"]], "Outliers in the X direction": [[217, "outliers-in-the-x-direction"]], "Tweedie regression on insurance claims": [[218, "tweedie-regression-on-insurance-claims"]], "Loading datasets, basic feature extraction and target definitions": [[218, "loading-datasets-basic-feature-extraction-and-target-definitions"]], "Frequency model \u2013 Poisson distribution": [[218, "frequency-model-poisson-distribution"]], "Severity Model -  Gamma distribution": [[218, "severity-model-gamma-distribution"]], "Pure Premium Modeling via a Product Model vs single TweedieRegressor": [[218, "pure-premium-modeling-via-a-product-model-vs-single-tweedieregressor"]], "Comparison of Manifold Learning methods": [[221, "comparison-of-manifold-learning-methods"]], "Define algorithms for the manifold learning": [[221, "define-algorithms-for-the-manifold-learning"]], "Locally Linear Embeddings": [[221, "locally-linear-embeddings"]], "Isomap Embedding": [[221, "isomap-embedding"]], "Multidimensional scaling": [[221, "multidimensional-scaling"]], "Spectral embedding for non-linear dimensionality reduction": [[221, "spectral-embedding-for-non-linear-dimensionality-reduction"]], "T-distributed Stochastic Neighbor Embedding": [[221, "t-distributed-stochastic-neighbor-embedding"]], "Manifold learning on handwritten digits: Locally Linear Embedding, Isomap\u2026": [[222, "manifold-learning-on-handwritten-digits-locally-linear-embedding-isomap"]], "Load digits dataset": [[222, "load-digits-dataset"]], "Helper function to plot embedding": [[222, "helper-function-to-plot-embedding"]], "Embedding techniques comparison": [[222, "embedding-techniques-comparison"]], "Manifold Learning methods on a severed sphere": [[223, "manifold-learning-methods-on-a-severed-sphere"]], "Multi-dimensional scaling": [[224, "multi-dimensional-scaling"]], "Swiss Roll And Swiss-Hole Reduction": [[225, "swiss-roll-and-swiss-hole-reduction"]], "Swiss Roll": [[225, "swiss-roll"]], "Swiss-Hole": [[225, "swiss-hole"]], "t-SNE: The effect of various perplexity values on the shape": [[226, "t-sne-the-effect-of-various-perplexity-values-on-the-shape"]], "Comparing anomaly detection algorithms for outlier detection on toy datasets": [[229, "comparing-anomaly-detection-algorithms-for-outlier-detection-on-toy-datasets"]], "Visualizations with Display Objects": [[230, "visualizations-with-display-objects"]], "Load Data and train model": [[230, "load-data-and-train-model"]], "Create ConfusionMatrixDisplay": [[230, "create-confusionmatrixdisplay"]], "Create RocCurveDisplay": [[230, "create-roccurvedisplay"]], "Create PrecisionRecallDisplay": [[230, "create-precisionrecalldisplay"]], "Combining the display objects into a single plot": [[230, "combining-the-display-objects-into-a-single-plot"]], "Displaying estimators and complex pipelines": [[231, "displaying-estimators-and-complex-pipelines"]], "Compact text representation": [[231, "compact-text-representation"]], "Rich HTML representation": [[231, "rich-html-representation"]], "Isotonic Regression": [[232, "isotonic-regression"]], "The Johnson-Lindenstrauss bound for embedding with random projections": [[233, "the-johnson-lindenstrauss-bound-for-embedding-with-random-projections"]], "Theoretical bounds": [[233, "theoretical-bounds"]], "Empirical validation": [[233, "empirical-validation"]], "Remarks": [[233, "remarks"]], "Explicit feature map approximation for RBF kernels": [[234, "explicit-feature-map-approximation-for-rbf-kernels"]], "Python package and dataset imports, load dataset": [[234, "python-package-and-dataset-imports-load-dataset"]], "Timing and accuracy plots": [[234, "timing-and-accuracy-plots"]], "Decision Surfaces of RBF Kernel SVM and Linear SVM": [[234, "decision-surfaces-of-rbf-kernel-svm-and-linear-svm"]], "Comparison of kernel ridge regression and SVR": [[235, "comparison-of-kernel-ridge-regression-and-svr"]], "Construct the kernel-based regression models": [[235, "construct-the-kernel-based-regression-models"]], "Compare times of SVR and Kernel Ridge Regression": [[235, "compare-times-of-svr-and-kernel-ridge-regression"]], "Look at the results": [[235, "look-at-the-results"], [346, "look-at-the-results"]], "Visualize training and prediction times": [[235, "visualize-training-and-prediction-times"]], "Visualize the learning curves": [[235, "visualize-the-learning-curves"]], "Metadata Routing": [[236, "metadata-routing"], [322, "metadata-routing"], [395, "metadata-routing"], [399, "id1"], [1048, "metadata-routing"]], "Estimators": [[236, "estimators"], [363, "estimators"], [363, "id2"], [383, "estimators"], [404, "estimators"], [407, "estimators"]], "Router and Consumer": [[236, "router-and-consumer"]], "Simple Pipeline": [[236, "simple-pipeline"]], "Deprecation / Default Value Change": [[236, "deprecation-default-value-change"]], "Third Party Development and scikit-learn Dependency": [[236, "third-party-development-and-scikit-learn-dependency"]], "Multilabel classification": [[237, "multilabel-classification"], [989, "multilabel-classification"]], "Face completion with a multi-output estimators": [[238, "face-completion-with-a-multi-output-estimators"]], "Evaluation of outlier detection estimators": [[239, "evaluation-of-outlier-detection-estimators"]], "Dataset preprocessing and model training": [[239, "dataset-preprocessing-and-model-training"]], "KDDCup99 - SA dataset": [[239, "kddcup99-sa-dataset"]], "Forest covertypes dataset": [[239, "forest-covertypes-dataset"]], "Ames Housing dataset": [[239, "ames-housing-dataset"]], "Cardiotocography dataset": [[239, "cardiotocography-dataset"]], "Plot and interpret results": [[239, "plot-and-interpret-results"]], "Ablation study": [[239, "ablation-study"]], "Advanced Plotting With Partial Dependence": [[240, "advanced-plotting-with-partial-dependence"]], "Train models on the diabetes dataset": [[240, "train-models-on-the-diabetes-dataset"]], "Plotting partial dependence for two features": [[240, "plotting-partial-dependence-for-two-features"]], "Plotting partial dependence of the two models together": [[240, "plotting-partial-dependence-of-the-two-models-together"]], "Plotting partial dependence for one feature": [[240, "plotting-partial-dependence-for-one-feature"]], "Displaying Pipelines": [[241, "displaying-pipelines"]], "Displaying a Pipeline with a Preprocessing Step and Classifier": [[241, "displaying-a-pipeline-with-a-preprocessing-step-and-classifier"]], "Displaying a Pipeline Chaining Multiple Preprocessing Steps & Classifier": [[241, "displaying-a-pipeline-chaining-multiple-preprocessing-steps-classifier"]], "Displaying a Pipeline and Dimensionality Reduction and Classifier": [[241, "displaying-a-pipeline-and-dimensionality-reduction-and-classifier"]], "Displaying a Complex Pipeline Chaining a Column Transformer": [[241, "displaying-a-complex-pipeline-chaining-a-column-transformer"]], "Displaying a Grid Search over a Pipeline with a Classifier": [[241, "displaying-a-grid-search-over-a-pipeline-with-a-classifier"]], "ROC Curve with Visualization API": [[242, "roc-curve-with-visualization-api"]], "Load Data and Train a SVC": [[242, "load-data-and-train-a-svc"]], "Plotting the ROC Curve": [[242, "plotting-the-roc-curve"]], "Training a Random Forest and Plotting the ROC Curve": [[242, "training-a-random-forest-and-plotting-the-roc-curve"]], "Introducing the set_output API": [[243, "introducing-the-set-output-api"]], "Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture": [[246, "concentration-prior-type-analysis-of-variation-bayesian-gaussian-mixture"]], "Gaussian Mixture Model Ellipsoids": [[247, "gaussian-mixture-model-ellipsoids"]], "GMM covariances": [[248, "gmm-covariances"]], "GMM Initialization Methods": [[249, "gmm-initialization-methods"]], "Density Estimation for a Gaussian mixture": [[250, "density-estimation-for-a-gaussian-mixture"]], "Gaussian Mixture Model Selection": [[251, "gaussian-mixture-model-selection"]], "Plot the BIC scores": [[251, "plot-the-bic-scores"]], "Plot the best model": [[251, "plot-the-best-model"]], "Gaussian Mixture Model Sine Curve": [[252, "gaussian-mixture-model-sine-curve"]], "Confusion matrix": [[255, "confusion-matrix"], [988, "confusion-matrix"]], "Visualizing cross-validation behavior in scikit-learn": [[256, "visualizing-cross-validation-behavior-in-scikit-learn"]], "Visualize our data": [[256, "visualize-our-data"]], "Define a function to visualize cross-validation behavior": [[256, "define-a-function-to-visualize-cross-validation-behavior"]], "Visualize cross-validation indices for many CV objects": [[256, "visualize-cross-validation-indices-for-many-cv-objects"]], "Plotting Cross-Validated Predictions": [[257, "plotting-cross-validated-predictions"]], "Detection error tradeoff (DET) curve": [[258, "detection-error-tradeoff-det-curve"]], "Generate synthetic data": [[258, "generate-synthetic-data"]], "Define the classifiers": [[258, "define-the-classifiers"]], "Plot ROC and DET curves": [[258, "plot-roc-and-det-curves"]], "Custom refit strategy of a grid search with cross-validation": [[259, "custom-refit-strategy-of-a-grid-search-with-cross-validation"]], "The dataset": [[259, "the-dataset"]], "Define our grid-search strategy": [[259, "define-our-grid-search-strategy"]], "Tuning hyper-parameters": [[259, "tuning-hyper-parameters"]], "Balance model complexity and cross-validated score": [[260, "balance-model-complexity-and-cross-validated-score"]], "Statistical comparison of models using grid search": [[261, "statistical-comparison-of-models-using-grid-search"]], "Comparing two models: frequentist approach": [[261, "comparing-two-models-frequentist-approach"]], "Comparing two models: Bayesian approach": [[261, "comparing-two-models-bayesian-approach"]], "Region of Practical Equivalence": [[261, "region-of-practical-equivalence"]], "Pairwise comparison of all models: frequentist approach": [[261, "pairwise-comparison-of-all-models-frequentist-approach"]], "Pairwise comparison of all models: Bayesian approach": [[261, "pairwise-comparison-of-all-models-bayesian-approach"]], "Take-home messages": [[261, "take-home-messages"]], "Sample pipeline for text feature extraction and evaluation": [[262, "sample-pipeline-for-text-feature-extraction-and-evaluation"]], "Data loading": [[262, "data-loading"]], "Pipeline with hyperparameter tuning": [[262, "pipeline-with-hyperparameter-tuning"]], "Plotting Learning Curves and Checking Models\u2019 Scalability": [[263, "plotting-learning-curves-and-checking-models-scalability"]], "Learning Curve": [[263, "learning-curve"]], "Complexity analysis": [[263, "complexity-analysis"]], "Class Likelihood Ratios to measure classification performance": [[264, "class-likelihood-ratios-to-measure-classification-performance"]], "Pre-test vs. post-test analysis": [[264, "pre-test-vs-post-test-analysis"]], "Cross-validation of likelihood ratios": [[264, "cross-validation-of-likelihood-ratios"]], "Invariance with respect to prevalence": [[264, "invariance-with-respect-to-prevalence"]], "Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV": [[265, "demonstration-of-multi-metric-evaluation-on-cross-val-score-and-gridsearchcv"]], "Running GridSearchCV using multiple evaluation metrics": [[265, "running-gridsearchcv-using-multiple-evaluation-metrics"]], "Plotting the result": [[265, "plotting-the-result"]], "Nested versus non-nested cross-validation": [[266, "nested-versus-non-nested-cross-validation"]], "See Also:": [[266, null], [409, null]], "Test with permutations the significance of a classification score": [[267, "test-with-permutations-the-significance-of-a-classification-score"]], "Permutation test score": [[267, "permutation-test-score"], [412, "permutation-test-score"]], "Original data": [[267, "original-data"], [306, "original-data"]], "Random data": [[267, "random-data"]], "Precision-Recall": [[268, "precision-recall"]], "In binary classification settings": [[268, "in-binary-classification-settings"]], "Dataset and model": [[268, "dataset-and-model"]], "Plot the Precision-Recall curve": [[268, "plot-the-precision-recall-curve"]], "In multi-label settings": [[268, "in-multi-label-settings"]], "Create multi-label data, fit, and predict": [[268, "create-multi-label-data-fit-and-predict"]], "The average precision score in multi-label settings": [[268, "the-average-precision-score-in-multi-label-settings"]], "Plot the micro-averaged Precision-Recall curve": [[268, "plot-the-micro-averaged-precision-recall-curve"]], "Plot Precision-Recall curve for each class and iso-f1 curves": [[268, "plot-precision-recall-curve-for-each-class-and-iso-f1-curves"]], "Comparing randomized search and grid search for hyperparameter estimation": [[269, "comparing-randomized-search-and-grid-search-for-hyperparameter-estimation"]], "Multiclass Receiver Operating Characteristic (ROC)": [[270, "multiclass-receiver-operating-characteristic-roc"]], "Load and prepare data": [[270, "load-and-prepare-data"], [271, "load-and-prepare-data"], [311, "load-and-prepare-data"]], "One-vs-Rest multiclass ROC": [[270, "one-vs-rest-multiclass-roc"]], "ROC curve showing a specific class": [[270, "roc-curve-showing-a-specific-class"]], "ROC curve using micro-averaged OvR": [[270, "roc-curve-using-micro-averaged-ovr"]], "ROC curve using the OvR macro-average": [[270, "roc-curve-using-the-ovr-macro-average"]], "Plot all OvR ROC curves together": [[270, "plot-all-ovr-roc-curves-together"]], "One-vs-One multiclass ROC": [[270, "one-vs-one-multiclass-roc"]], "ROC curve using the OvO macro-average": [[270, "roc-curve-using-the-ovo-macro-average"]], "Plot all OvO ROC curves together": [[270, "plot-all-ovo-roc-curves-together"]], "Receiver Operating Characteristic (ROC) with cross validation": [[271, "receiver-operating-characteristic-roc-with-cross-validation"]], "Classification and ROC analysis": [[271, "classification-and-roc-analysis"]], "Comparison between grid search and successive halving": [[272, "comparison-between-grid-search-and-successive-halving"]], "Successive Halving Iterations": [[273, "successive-halving-iterations"]], "Number of candidates and amount of resource at each iteration": [[273, "number-of-candidates-and-amount-of-resource-at-each-iteration"]], "Train error vs Test error": [[274, "train-error-vs-test-error"]], "Compute train and test errors": [[274, "compute-train-and-test-errors"]], "Plot results functions": [[274, "plot-results-functions"]], "Underfitting vs. Overfitting": [[275, "underfitting-vs-overfitting"]], "Plotting Validation Curves": [[276, "plotting-validation-curves"]], "Overview of multiclass training meta-estimators": [[279, "overview-of-multiclass-training-meta-estimators"]], "The Yeast UCI dataset": [[279, "the-yeast-uci-dataset"]], "Strategies comparison": [[279, "strategies-comparison"]], "The importance of hyperparameters search": [[279, "the-importance-of-hyperparameters-search"]], "Multilabel classification using a classifier chain": [[282, "multilabel-classification-using-a-classifier-chain"]], "Loading a dataset": [[282, "loading-a-dataset"]], "LogisticRegression wrapped by OneVsRestClassifier": [[282, "logisticregression-wrapped-by-onevsrestclassifier"]], "Chain of binary classifiers": [[282, "chain-of-binary-classifiers"]], "Results interpretation": [[282, "results-interpretation"]], "Approximate nearest neighbors in TSNE": [[284, "approximate-nearest-neighbors-in-tsne"]], "Caching nearest neighbors": [[286, "caching-nearest-neighbors"]], "Nearest Neighbors Classification": [[287, "nearest-neighbors-classification"], [991, "nearest-neighbors-classification"]], "K-nearest neighbors classifier": [[287, "k-nearest-neighbors-classifier"]], "Decision boundary": [[287, "decision-boundary"]], "Kernel Density Estimation": [[288, "kernel-density-estimation"], [414, "kernel-density-estimation"]], "Simple 1D Kernel Density Estimation": [[289, "simple-1d-kernel-density-estimation"]], "Novelty detection with Local Outlier Factor (LOF)": [[290, "novelty-detection-with-local-outlier-factor-lof"]], "Outlier detection with Local Outlier Factor (LOF)": [[291, "outlier-detection-with-local-outlier-factor-lof"]], "Generate data with outliers": [[291, "generate-data-with-outliers"]], "Fit the model for outlier detection (default)": [[291, "fit-the-model-for-outlier-detection-default"]], "Comparing Nearest Neighbors with and without Neighborhood Components Analysis": [[292, "comparing-nearest-neighbors-with-and-without-neighborhood-components-analysis"]], "Dimensionality Reduction with Neighborhood Components Analysis": [[293, "dimensionality-reduction-with-neighborhood-components-analysis"]], "Neighborhood Components Analysis Illustration": [[294, "neighborhood-components-analysis-illustration"]], "Original points": [[294, "original-points"]], "Learning an embedding": [[294, "learning-an-embedding"]], "Nearest Centroid Classification": [[295, "nearest-centroid-classification"]], "Nearest Neighbors regression": [[296, "nearest-neighbors-regression"]], "Kernel Density Estimate of Species Distributions": [[297, "kernel-density-estimate-of-species-distributions"]], "Varying regularization in Multi-layer Perceptron": [[300, "varying-regularization-in-multi-layer-perceptron"]], "Compare Stochastic learning strategies for MLPClassifier": [[301, "compare-stochastic-learning-strategies-for-mlpclassifier"]], "Visualization of MLP weights on MNIST": [[302, "visualization-of-mlp-weights-on-mnist"]], "Restricted Boltzmann Machine features for digit classification": [[303, "restricted-boltzmann-machine-features-for-digit-classification"]], "Models definition": [[303, "models-definition"]], "Training": [[303, "training"], [991, "training"]], "Plotting": [[303, "plotting"], [407, "plotting"], [407, "id7"], [407, "id9"]], "Compare the effect of different scalers on data with outliers": [[306, "compare-the-effect-of-different-scalers-on-data-with-outliers"]], "StandardScaler": [[306, "standardscaler"]], "MinMaxScaler": [[306, "minmaxscaler"]], "MaxAbsScaler": [[306, "maxabsscaler"]], "RobustScaler": [[306, "robustscaler"]], "PowerTransformer": [[306, "powertransformer"]], "QuantileTransformer (uniform output)": [[306, "quantiletransformer-uniform-output"]], "QuantileTransformer (Gaussian output)": [[306, "quantiletransformer-gaussian-output"]], "Normalizer": [[306, "normalizer"]], "Using KBinsDiscretizer to discretize continuous features": [[307, "using-kbinsdiscretizer-to-discretize-continuous-features"]], "Feature discretization": [[308, "feature-discretization"]], "Demonstrating the different strategies of KBinsDiscretizer": [[309, "demonstrating-the-different-strategies-of-kbinsdiscretizer"]], "Map data to a normal distribution": [[310, "map-data-to-a-normal-distribution"]], "Importance of Feature Scaling": [[311, "importance-of-feature-scaling"]], "Effect of rescaling on a k-neighbors models": [[311, "effect-of-rescaling-on-a-k-neighbors-models"]], "Effect of rescaling on a PCA dimensional reduction": [[311, "effect-of-rescaling-on-a-pca-dimensional-reduction"]], "Effect of rescaling on model\u2019s performance": [[311, "effect-of-rescaling-on-model-s-performance"]], "Comparing Target Encoder with Other Encoders": [[312, "comparing-target-encoder-with-other-encoders"]], "Loading Data from OpenML": [[312, "loading-data-from-openml"]], "Training and Evaluating Pipelines with Different Encoders": [[312, "training-and-evaluating-pipelines-with-different-encoders"]], "Native Categorical Feature Support": [[312, "native-categorical-feature-support"]], "Plotting the Results": [[312, "plotting-the-results"]], "Target Encoder\u2019s Internal Cross fitting": [[313, "target-encoder-s-internal-cross-fitting"]], "Create Synthetic Dataset": [[313, "create-synthetic-dataset"]], "Training a Ridge Regressor": [[313, "training-a-ridge-regressor"]], "Release Highlights for scikit-learn 0.22": [[316, "release-highlights-for-scikit-learn-0-22"]], "New plotting API": [[316, "new-plotting-api"]], "Stacking Classifier and Regressor": [[316, "stacking-classifier-and-regressor"]], "Permutation-based feature importance": [[316, "permutation-based-feature-importance"]], "Native support for missing values for gradient boosting": [[316, "native-support-for-missing-values-for-gradient-boosting"]], "Precomputed sparse nearest neighbors graph": [[316, "precomputed-sparse-nearest-neighbors-graph"]], "KNN Based Imputation": [[316, "knn-based-imputation"]], "Tree pruning": [[316, "tree-pruning"]], "Retrieve dataframes from OpenML": [[316, "retrieve-dataframes-from-openml"]], "Checking scikit-learn compatibility of an estimator": [[316, "checking-scikit-learn-compatibility-of-an-estimator"]], "ROC AUC now supports multiclass classification": [[316, "roc-auc-now-supports-multiclass-classification"]], "Release Highlights for scikit-learn 0.23": [[317, "release-highlights-for-scikit-learn-0-23"]], "Generalized Linear Models, and Poisson loss for gradient boosting": [[317, "generalized-linear-models-and-poisson-loss-for-gradient-boosting"]], "Rich visual representation of estimators": [[317, "rich-visual-representation-of-estimators"]], "Scalability and stability improvements to KMeans": [[317, "scalability-and-stability-improvements-to-kmeans"]], "Improvements to the histogram-based Gradient Boosting estimators": [[317, "improvements-to-the-histogram-based-gradient-boosting-estimators"]], "Sample-weight support for Lasso and ElasticNet": [[317, "sample-weight-support-for-lasso-and-elasticnet"]], "Release Highlights for scikit-learn 0.24": [[318, "release-highlights-for-scikit-learn-0-24"]], "Successive Halving estimators for tuning hyper-parameters": [[318, "successive-halving-estimators-for-tuning-hyper-parameters"]], "Native support for categorical features in HistGradientBoosting estimators": [[318, "native-support-for-categorical-features-in-histgradientboosting-estimators"]], "Improved performances of HistGradientBoosting estimators": [[318, "improved-performances-of-histgradientboosting-estimators"]], "New self-training meta-estimator": [[318, "new-self-training-meta-estimator"]], "New SequentialFeatureSelector transformer": [[318, "new-sequentialfeatureselector-transformer"]], "New PolynomialCountSketch kernel approximation function": [[318, "new-polynomialcountsketch-kernel-approximation-function"]], "Individual Conditional Expectation plots": [[318, "individual-conditional-expectation-plots"]], "New Poisson splitting criterion for DecisionTreeRegressor": [[318, "new-poisson-splitting-criterion-for-decisiontreeregressor"]], "New documentation improvements": [[318, "new-documentation-improvements"], [319, "new-documentation-improvements"]], "Release Highlights for scikit-learn 1.0": [[319, "release-highlights-for-scikit-learn-1-0"]], "Keyword and positional arguments": [[319, "keyword-and-positional-arguments"]], "Spline Transformers": [[319, "spline-transformers"]], "Quantile Regressor": [[319, "quantile-regressor"]], "Feature Names Support": [[319, "feature-names-support"]], "A more flexible plotting API": [[319, "a-more-flexible-plotting-api"]], "Online One-Class SVM": [[319, "online-one-class-svm"], [1002, "online-one-class-svm"]], "Histogram-based Gradient Boosting Models are now stable": [[319, "histogram-based-gradient-boosting-models-are-now-stable"]], "Release Highlights for scikit-learn 1.1": [[320, "release-highlights-for-scikit-learn-1-1"]], "Quantile loss in ensemble.HistGradientBoostingRegressor": [[320, "quantile-loss-in-ensemble-histgradientboostingregressor"]], "get_feature_names_out Available in all Transformers": [[320, "get-feature-names-out-available-in-all-transformers"]], "Grouping infrequent categories in OneHotEncoder": [[320, "grouping-infrequent-categories-in-onehotencoder"]], "Performance improvements": [[320, "performance-improvements"]], "MiniBatchNMF: an online version of NMF": [[320, "minibatchnmf-an-online-version-of-nmf"]], "BisectingKMeans: divide and cluster": [[320, "bisectingkmeans-divide-and-cluster"]], "Release Highlights for scikit-learn 1.2": [[321, "release-highlights-for-scikit-learn-1-2"]], "Pandas output with set_output API": [[321, "pandas-output-with-set-output-api"]], "Interaction constraints in Histogram-based Gradient Boosting Trees": [[321, "interaction-constraints-in-histogram-based-gradient-boosting-trees"]], "New and enhanced displays": [[321, "new-and-enhanced-displays"]], "Faster parser in fetch_openml": [[321, "faster-parser-in-fetch-openml"]], "Experimental Array API support in LinearDiscriminantAnalysis": [[321, "experimental-array-api-support-in-lineardiscriminantanalysis"]], "Improved efficiency of many estimators": [[321, "improved-efficiency-of-many-estimators"]], "Release Highlights for scikit-learn 1.3": [[322, "release-highlights-for-scikit-learn-1-3"]], "HDBSCAN: hierarchical density-based clustering": [[322, "hdbscan-hierarchical-density-based-clustering"]], "TargetEncoder: a new category encoding strategy": [[322, "targetencoder-a-new-category-encoding-strategy"]], "Missing values support in decision trees": [[322, "missing-values-support-in-decision-trees"]], "New display model_selection.ValidationCurveDisplay": [[322, "new-display-model-selection-validationcurvedisplay"]], "Gamma loss for gradient boosting": [[322, "gamma-loss-for-gradient-boosting"]], "Grouping infrequent categories in preprocessing.OrdinalEncoder": [[322, "grouping-infrequent-categories-in-preprocessing-ordinalencoder"]], "Release Highlights for scikit-learn 1.4": [[323, "release-highlights-for-scikit-learn-1-4"]], "HistGradientBoosting Natively Supports Categorical DTypes in DataFrames": [[323, "histgradientboosting-natively-supports-categorical-dtypes-in-dataframes"]], "Polars output in set_output": [[323, "polars-output-in-set-output"]], "Missing value support for Random Forest": [[323, "missing-value-support-for-random-forest"]], "Add support for monotonic constraints in tree-based models": [[323, "add-support-for-monotonic-constraints-in-tree-based-models"]], "Enriched estimator displays": [[323, "enriched-estimator-displays"]], "Metadata Routing Support": [[323, "metadata-routing-support"]], "Label Propagation digits: Demonstrating performance": [[326, "label-propagation-digits-demonstrating-performance"]], "Semi-supervised learning": [[326, "semi-supervised-learning"], [1001, "semi-supervised-learning"]], "Plot the most uncertain predictions": [[326, "plot-the-most-uncertain-predictions"]], "Label Propagation digits active learning": [[327, "label-propagation-digits-active-learning"]], "Label Propagation learning a complex structure": [[328, "label-propagation-learning-a-complex-structure"]], "Effect of varying threshold for self-training": [[329, "effect-of-varying-threshold-for-self-training"]], "Semi-supervised Classification on a Text Dataset": [[330, "semi-supervised-classification-on-a-text-dataset"]], "Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset": [[331, "decision-boundary-of-semi-supervised-classifiers-versus-svm-on-the-iris-dataset"]], "SVM with custom kernel": [[335, "svm-with-custom-kernel"]], "Plot different SVM classifiers in the iris dataset": [[336, "plot-different-svm-classifiers-in-the-iris-dataset"]], "Plot the support vectors in LinearSVC": [[337, "plot-the-support-vectors-in-linearsvc"]], "One-class SVM with non-linear kernel (RBF)": [[338, "one-class-svm-with-non-linear-kernel-rbf"]], "RBF SVM parameters": [[339, "rbf-svm-parameters"]], "Load and prepare data set": [[339, "load-and-prepare-data-set"]], "Train classifiers": [[339, "train-classifiers"]], "SVM: Maximum margin separating hyperplane": [[340, "svm-maximum-margin-separating-hyperplane"]], "SVM: Separating hyperplane for unbalanced classes": [[341, "svm-separating-hyperplane-for-unbalanced-classes"]], "SVM-Anova: SVM with univariate feature selection": [[342, "svm-anova-svm-with-univariate-feature-selection"]], "Load some data to play with": [[342, "load-some-data-to-play-with"]], "Create the pipeline": [[342, "create-the-pipeline"]], "Plot the cross-validation score as a function of percentile of features": [[342, "plot-the-cross-validation-score-as-a-function-of-percentile-of-features"]], "Plot classification boundaries with different SVM Kernels": [[343, "plot-classification-boundaries-with-different-svm-kernels"]], "Creating a dataset": [[343, "creating-a-dataset"]], "Training SVC model and plotting decision boundaries": [[343, "training-svc-model-and-plotting-decision-boundaries"]], "Linear kernel": [[343, "linear-kernel"], [986, "linear-kernel"], [1022, "linear-kernel"]], "Polynomial kernel": [[343, "polynomial-kernel"], [986, "polynomial-kernel"], [1022, "polynomial-kernel"]], "RBF kernel": [[343, "rbf-kernel"], [986, "rbf-kernel"]], "Sigmoid kernel": [[343, "sigmoid-kernel"], [986, "sigmoid-kernel"], [1022, "sigmoid-kernel"]], "SVM Margins Example": [[344, "svm-margins-example"]], "Non-linear SVM": [[345, "non-linear-svm"]], "Support Vector Regression (SVR) using linear and non-linear kernels": [[346, "support-vector-regression-svr-using-linear-and-non-linear-kernels"]], "Scaling the regularization parameter for SVCs": [[347, "scaling-the-regularization-parameter-for-svcs"]], "L1-penalty case": [[347, "l1-penalty-case"]], "L2-penalty case": [[347, "l2-penalty-case"]], "SVM Tie Breaking Example": [[348, "svm-tie-breaking-example"]], "SVM: Weighted samples": [[349, "svm-weighted-samples"]], "Classification of text documents using sparse features": [[352, "classification-of-text-documents-using-sparse-features"]], "Loading and vectorizing the 20 newsgroups text dataset": [[352, "loading-and-vectorizing-the-20-newsgroups-text-dataset"]], "Analysis of a bag-of-words document classifier": [[352, "analysis-of-a-bag-of-words-document-classifier"]], "Model without metadata stripping": [[352, "model-without-metadata-stripping"]], "Model with metadata stripping": [[352, "model-with-metadata-stripping"]], "Benchmarking classifiers": [[352, "benchmarking-classifiers"]], "Plot accuracy, training and test time of each classifier": [[352, "plot-accuracy-training-and-test-time-of-each-classifier"]], "Clustering text documents using k-means": [[353, "clustering-text-documents-using-k-means"]], "Loading text data": [[353, "loading-text-data"]], "Quantifying the quality of clustering results": [[353, "quantifying-the-quality-of-clustering-results"]], "K-means clustering on text features": [[353, "k-means-clustering-on-text-features"]], "Feature Extraction using TfidfVectorizer": [[353, "feature-extraction-using-tfidfvectorizer"]], "Clustering sparse data with k-means": [[353, "clustering-sparse-data-with-k-means"]], "Performing dimensionality reduction using LSA": [[353, "performing-dimensionality-reduction-using-lsa"]], "Top terms per cluster": [[353, "top-terms-per-cluster"]], "HashingVectorizer": [[353, "hashingvectorizer"]], "Clustering evaluation summary": [[353, "clustering-evaluation-summary"]], "FeatureHasher and DictVectorizer Comparison": [[354, "featurehasher-and-dictvectorizer-comparison"]], "Load Data": [[354, "load-data"]], "Define preprocessing functions": [[354, "define-preprocessing-functions"]], "DictVectorizer": [[354, "dictvectorizer"]], "FeatureHasher": [[354, "featurehasher"]], "Comparison with special purpose text vectorizers": [[354, "comparison-with-special-purpose-text-vectorizers"]], "TfidfVectorizer": [[354, "tfidfvectorizer"]], "Post pruning decision trees with cost complexity pruning": [[357, "post-pruning-decision-trees-with-cost-complexity-pruning"]], "Total impurity of leaves vs effective alphas of pruned tree": [[357, "total-impurity-of-leaves-vs-effective-alphas-of-pruned-tree"]], "Accuracy vs alpha for training and testing sets": [[357, "accuracy-vs-alpha-for-training-and-testing-sets"]], "Plot the decision surface of decision trees trained on the iris dataset": [[358, "plot-the-decision-surface-of-decision-trees-trained-on-the-iris-dataset"]], "Decision Tree Regression": [[359, "decision-tree-regression"]], "Multi-output Decision Tree Regression": [[360, "multi-output-decision-tree-regression"]], "Understanding the decision tree structure": [[361, "understanding-the-decision-tree-structure"]], "Train tree classifier": [[361, "train-tree-classifier"]], "Tree structure": [[361, "tree-structure"]], "What is the values array used here?": [[361, "what-is-the-values-array-used-here"]], "Decision path": [[361, "decision-path"]], "Common pitfalls and recommended practices": [[363, "common-pitfalls-and-recommended-practices"]], "Inconsistent preprocessing": [[363, "inconsistent-preprocessing"]], "Data leakage": [[363, "data-leakage"]], "Data leakage during pre-processing": [[363, "data-leakage-during-pre-processing"]], "How to avoid data leakage": [[363, "how-to-avoid-data-leakage"]], "Controlling randomness": [[363, "controlling-randomness"]], "Using None or RandomState instances, and repeated calls to fit and split": [[363, "using-none-or-randomstate-instances-and-repeated-calls-to-fit-and-split"]], "CV splitters": [[363, "cv-splitters"], [363, "id3"]], "Common pitfalls and subtleties": [[363, "common-pitfalls-and-subtleties"]], "General recommendations": [[363, "general-recommendations"]], "Getting reproducible results across multiple executions": [[363, "getting-reproducible-results-across-multiple-executions"]], "Robustness of cross-validation results": [[363, "robustness-of-cross-validation-results"]], "Computing with scikit-learn": [[366, "computing-with-scikit-learn"]], "Computational Performance": [[367, "computational-performance"]], "Bulk versus Atomic mode": [[367, "bulk-versus-atomic-mode"]], "Configuring Scikit-learn for reduced validation overhead": [[367, "configuring-scikit-learn-for-reduced-validation-overhead"]], "Influence of the Number of Features": [[367, "influence-of-the-number-of-features"]], "Influence of the Input Data Representation": [[367, "influence-of-the-input-data-representation"]], "Influence of the Model Complexity": [[367, "influence-of-the-model-complexity"]], "Feature Extraction Latency": [[367, "feature-extraction-latency"]], "Prediction Throughput": [[367, "prediction-throughput"]], "Tips and Tricks": [[367, "tips-and-tricks"]], "Linear algebra libraries": [[367, "linear-algebra-libraries"]], "Limiting Working Memory": [[367, "limiting-working-memory"]], "Model Compression": [[367, "model-compression"]], "Model Reshaping": [[367, "model-reshaping"]], "Links": [[367, "links"]], "Parallelism, resource management, and configuration": [[368, "parallelism-resource-management-and-configuration"]], "Parallelism": [[368, "parallelism"], [977, "parallelism"]], "Higher-level parallelism with joblib": [[368, "higher-level-parallelism-with-joblib"]], "Lower-level parallelism with OpenMP": [[368, "lower-level-parallelism-with-openmp"]], "Parallel NumPy and SciPy routines from numerical libraries": [[368, "parallel-numpy-and-scipy-routines-from-numerical-libraries"]], "Oversubscription: spawning too many threads": [[368, "oversubscription-spawning-too-many-threads"]], "Configuration switches": [[368, "configuration-switches"]], "Python API": [[368, "python-api"]], "Environment variables": [[368, "environment-variables"]], "SKLEARN_ASSUME_FINITE": [[368, "sklearn-assume-finite"]], "SKLEARN_WORKING_MEMORY": [[368, "sklearn-working-memory"]], "SKLEARN_SEED": [[368, "sklearn-seed"]], "SKLEARN_TESTS_GLOBAL_RANDOM_SEED": [[368, "sklearn-tests-global-random-seed"]], "SKLEARN_SKIP_NETWORK_TESTS": [[368, "sklearn-skip-network-tests"]], "SKLEARN_RUN_FLOAT32_TESTS": [[368, "sklearn-run-float32-tests"]], "SKLEARN_ENABLE_DEBUG_CYTHON_DIRECTIVES": [[368, "sklearn-enable-debug-cython-directives"]], "SKLEARN_BUILD_ENABLE_DEBUG_SYMBOLS": [[368, "sklearn-build-enable-debug-symbols"]], "SKLEARN_PAIRWISE_DIST_CHUNK_SIZE": [[368, "sklearn-pairwise-dist-chunk-size"]], "SKLEARN_DOC_BUILD_WARNINGS_AS_ERRORS": [[368, "sklearn-doc-build-warnings-as-errors"]], "Strategies to scale computationally: bigger data": [[369, "strategies-to-scale-computationally-bigger-data"]], "Scaling with instances using out-of-core learning": [[369, "scaling-with-instances-using-out-of-core-learning"]], "Streaming instances": [[369, "streaming-instances"]], "Extracting features": [[369, "extracting-features"]], "Incremental learning": [[369, "incremental-learning"]], "Notes": [[369, "notes"]], "Table Of Contents": [[370, "table-of-contents"]], "Dataset transformations": [[373, "dataset-transformations"]], "Dataset loading utilities": [[374, "dataset-loading-utilities"], [386, "dataset-loading-utilities"]], "Loading other datasets": [[375, "loading-other-datasets"]], "Sample images": [[375, "sample-images"]], "Examples:": [[375, null], [376, null], [397, null], [405, null], [405, null], [406, null], [408, null], [408, null], [408, null], [408, null], [408, null], [408, null], 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[987, null], [987, null], [987, null], [987, null], [988, null], [988, null], [988, null], [988, null], [988, null], [989, null], [991, null], [991, null], [991, null], [991, null], [991, null], [992, null], [992, null], [993, null], [994, null], [994, null], [994, null], [994, null], [994, null], [994, null], [995, null], [996, null], [998, null], [998, null], [998, null], [1002, null], [1002, null], [1003, null], [1003, null], [1003, null], [1003, null], [1003, null], [1003, null], [1004, null], [1004, null], [1004, null], [1004, null], [1028, null]], "Datasets in svmlight / libsvm format": [[375, "datasets-in-svmlight-libsvm-format"]], "Related links:": [[375, null]], "Downloading datasets from the openml.org repository": [[375, "downloading-datasets-from-the-openml-org-repository"]], "Dataset Versions": [[375, "dataset-versions"]], "ARFF parser": [[375, "arff-parser"]], "Loading from external datasets": [[375, "loading-from-external-datasets"], [1015, null]], "Real world datasets": [[376, "real-world-datasets"]], "The Olivetti faces dataset": [[376, "the-olivetti-faces-dataset"]], "The 20 newsgroups text dataset": [[376, "the-20-newsgroups-text-dataset"]], "Data Considerations": [[376, null]], "Recommendation": [[376, null]], "The Labeled Faces in the Wild face recognition dataset": [[376, "the-labeled-faces-in-the-wild-face-recognition-dataset"]], "Forest covertypes": [[376, "forest-covertypes"]], "RCV1 dataset": [[376, "rcv1-dataset"]], "Kddcup 99 dataset": [[376, "kddcup-99-dataset"]], "California Housing dataset": [[376, "california-housing-dataset"]], "Generated datasets": [[377, "generated-datasets"]], "Generators for classification and clustering": [[377, "generators-for-classification-and-clustering"]], "Single label": [[377, "single-label"]], "Multilabel": [[377, "multilabel"]], "Generators for regression": [[377, "generators-for-regression"]], "Generators for manifold learning": [[377, "generators-for-manifold-learning"]], "Generators for decomposition": [[377, "generators-for-decomposition"]], "Toy datasets": [[378, "toy-datasets"]], "Iris plants dataset": [[378, "iris-plants-dataset"]], "Diabetes dataset": [[378, "diabetes-dataset"], [1022, null]], "Optical recognition of handwritten digits dataset": [[378, "optical-recognition-of-handwritten-digits-dataset"]], "Linnerrud dataset": [[378, "linnerrud-dataset"]], "Wine recognition dataset": [[378, "wine-recognition-dataset"]], "Breast cancer wisconsin (diagnostic) dataset": [[378, "breast-cancer-wisconsin-diagnostic-dataset"]], "Installing the development version of scikit-learn": [[379, "installing-the-development-version-of-scikit-learn"]], "Installing nightly builds": [[379, "installing-nightly-builds"]], "Building from source": [[379, "building-from-source"]], "Dependencies": [[379, "dependencies"]], "Runtime dependencies": [[379, "runtime-dependencies"]], "Build dependencies": [[379, "build-dependencies"]], "Test dependencies": [[379, "test-dependencies"]], "Building a specific version from a tag": [[379, "building-a-specific-version-from-a-tag"]], "Editable mode": [[379, "editable-mode"]], "Platform-specific instructions": [[379, "platform-specific-instructions"]], "Windows": [[379, "windows"]], "macOS": [[379, "macos"]], "macOS compilers from conda-forge": [[379, "macos-compilers-from-conda-forge"]], "macOS compilers from Homebrew": [[379, "macos-compilers-from-homebrew"]], "Linux": [[379, "linux"]], "Linux compilers from the system": [[379, "linux-compilers-from-the-system"]], "Linux compilers from conda-forge": [[379, "linux-compilers-from-conda-forge"]], "FreeBSD": [[379, "freebsd"]], "Alternative compilers": [[379, "alternative-compilers"]], "Parallel builds": [[379, "parallel-builds"]], "Bug triaging and issue curation": [[380, "bug-triaging-and-issue-curation"]], "Working on issues to improve them": [[380, "working-on-issues-to-improve-them"]], "Fruitful discussions": [[380, null]], "Working on PRs to help review": [[380, "working-on-prs-to-help-review"]], "Triaging operations for members of the core and contributor experience teams": [[380, "triaging-operations-for-members-of-the-core-and-contributor-experience-teams"]], "Closing issues: a tough call": [[380, null]], "A typical workflow for triaging issues": [[380, "a-typical-workflow-for-triaging-issues"]], "Contributing": [[381, "contributing"], [393, "contributing"]], "Our community, our values": [[381, null]], "Ways to contribute": [[381, "ways-to-contribute"]], "Contributing to related projects": [[381, null]], "Submitting a bug report or a feature request": [[381, "submitting-a-bug-report-or-a-feature-request"]], "How to make a good bug report": [[381, "how-to-make-a-good-bug-report"]], "Contributing code": [[381, "contributing-code"]], "Video resources": [[381, "video-resources"]], "How to contribute": [[381, "how-to-contribute"]], "Learning git:": [[381, null]], "Pull request checklist": [[381, "pull-request-checklist"]], "Continuous Integration (CI)": [[381, "continuous-integration-ci"]], "Stalled pull requests": [[381, "stalled-pull-requests"]], "Stalled and Unclaimed Issues": [[381, "stalled-and-unclaimed-issues"]], "Issues for New Contributors": [[381, "issues-for-new-contributors"]], "good first issue tag": [[381, null]], "Easy tag": [[381, null]], "help wanted tag": [[381, null]], "Documentation": [[381, "documentation"], [1031, "documentation"]], "Building the documentation": [[381, "building-the-documentation"]], "Generated documentation on GitHub Actions": [[381, "generated-documentation-on-github-actions"]], "Testing and improving test coverage": [[381, "testing-and-improving-test-coverage"]], "Writing matplotlib related tests": [[381, "writing-matplotlib-related-tests"]], "Workflow to improve test coverage": [[381, "workflow-to-improve-test-coverage"]], "Monitoring performance": [[381, "monitoring-performance"]], "Issue Tracker Tags": [[381, "issue-tracker-tags"]], "Maintaining backwards compatibility": [[381, "maintaining-backwards-compatibility"]], "Deprecation": [[381, "deprecation"]], "Change the default value of a parameter": [[381, "change-the-default-value-of-a-parameter"]], "Code Review Guidelines": [[381, "code-review-guidelines"]], "Communication Guidelines": [[381, "communication-guidelines"]], "Reading the existing code base": [[381, "reading-the-existing-code-base"]], "Cython Best Practices, Conventions and Knowledge": [[382, "cython-best-practices-conventions-and-knowledge"]], "Tips for developing with Cython in scikit-learn": [[382, "tips-for-developing-with-cython-in-scikit-learn"]], "Tips to ease development": [[382, "tips-to-ease-development"]], "Tips for performance": [[382, "tips-for-performance"]], "Using OpenMP": [[382, "using-openmp"]], "Developing scikit-learn estimators": [[383, "developing-scikit-learn-estimators"]], "APIs of scikit-learn objects": [[383, "apis-of-scikit-learn-objects"]], "Different objects": [[383, "different-objects"]], "Instantiation": [[383, "instantiation"]], "Fitting": [[383, "fitting"]], "Estimated Attributes": [[383, "estimated-attributes"]], "Optional Arguments": [[383, "optional-arguments"]], "Universal attributes": [[383, "universal-attributes"]], "Rolling your own estimator": [[383, "rolling-your-own-estimator"]], "Project template:": [[383, null]], "BaseEstimator and mixins:": [[383, null]], "get_params and set_params": [[383, "get-params-and-set-params"]], "Parameters and init": [[383, "parameters-and-init"]], "Cloning": [[383, "cloning"]], "Pipeline compatibility": [[383, "pipeline-compatibility"]], "Estimator types": [[383, "estimator-types"]], "Specific models": [[383, "specific-models"]], "Estimator Tags": [[383, "estimator-tags"]], "Developer API for set_output": [[383, "developer-api-for-set-output"]], "Developer API for check_is_fitted": [[383, "developer-api-for-check-is-fitted"]], "Developer API for HTML representation": [[383, "developer-api-for-html-representation"]], "Coding guidelines": [[383, "coding-guidelines"]], "Input validation": [[383, "input-validation"]], "Random Numbers": [[383, "random-numbers"]], "Numerical assertions in tests": [[383, "numerical-assertions-in-tests"]], "Developer\u2019s Guide": [[384, "developer-s-guide"]], "Maintainer / core-developer information": [[385, "maintainer-core-developer-information"]], "Releasing": [[385, "releasing"]], "Before a release": [[385, "before-a-release"]], "Preparing a release PR": [[385, "preparing-a-release-pr"]], "Major version release": [[385, "major-version-release"]], "Minor version release (also known as bug-fix release)": [[385, "minor-version-release-also-known-as-bug-fix-release"]], "Making a release": [[385, "making-a-release"]], "Release checklist": [[385, "release-checklist"]], "Merging Pull Requests": [[385, "merging-pull-requests"]], "The scikit-learn.org web site": [[385, "the-scikit-learn-org-web-site"]], "Experimental features": [[385, "experimental-features"]], "Crafting a minimal reproducer for scikit-learn": [[386, "crafting-a-minimal-reproducer-for-scikit-learn"]], "Good practices": [[386, "good-practices"]], "Provide a failing code example with minimal comments": [[386, "provide-a-failing-code-example-with-minimal-comments"]], "Boil down your script to something as small as possible": [[386, "boil-down-your-script-to-something-as-small-as-possible"]], "DO NOT report your data unless it is extremely necessary": [[386, "do-not-report-your-data-unless-it-is-extremely-necessary"]], "Use markdown formatting": [[386, "use-markdown-formatting"]], "Synthetic dataset": [[386, "synthetic-dataset"]], "NumPy": [[386, "numpy"]], "Pandas": [[386, "pandas"]], "make_regression": [[386, "make-regression"]], "make_classification": [[386, "make-classification"]], "make_blobs": [[386, "make-blobs"]], "How to optimize for speed": [[387, "how-to-optimize-for-speed"]], "Python, Cython or C/C++?": [[387, "python-cython-or-c-c"]], "Profiling Python code": [[387, "profiling-python-code"]], "Memory usage profiling": [[387, "memory-usage-profiling"]], "Using Cython": [[387, "using-cython"]], "Profiling compiled extensions": [[387, "profiling-compiled-extensions"]], "Using yep and gperftools": [[387, "using-yep-and-gperftools"]], "Using a debugger, gdb": [[387, "using-a-debugger-gdb"]], "Using gprof": [[387, "using-gprof"]], "Using valgrind / callgrind / kcachegrind": [[387, "using-valgrind-callgrind-kcachegrind"]], "kcachegrind": [[387, "kcachegrind"]], "Multi-core parallelism using joblib.Parallel": [[387, "multi-core-parallelism-using-joblib-parallel"]], "A simple algorithmic trick: warm restarts": [[387, "a-simple-algorithmic-trick-warm-restarts"]], "Developing with the Plotting API": [[388, "developing-with-the-plotting-api"]], "Plotting API Overview": [[388, "plotting-api-overview"]], "Plotting with Multiple Axes": [[388, "plotting-with-multiple-axes"]], "Developers\u2019 Tips and Tricks": [[389, "developers-tips-and-tricks"]], "Productivity and sanity-preserving tips": [[389, "productivity-and-sanity-preserving-tips"]], "Folding and unfolding outdated diffs on pull requests": [[389, "folding-and-unfolding-outdated-diffs-on-pull-requests"]], "Checking out pull requests as remote-tracking branches": [[389, "checking-out-pull-requests-as-remote-tracking-branches"]], "Display code coverage in pull requests": [[389, "display-code-coverage-in-pull-requests"]], "Useful pytest aliases and flags": [[389, "useful-pytest-aliases-and-flags"]], "Standard replies for reviewing": [[389, "standard-replies-for-reviewing"]], "Debugging memory errors in Cython with valgrind": [[389, "debugging-memory-errors-in-cython-with-valgrind"]], "Building and testing for the ARM64 platform on a x86_64 machine": [[389, "building-and-testing-for-the-arm64-platform-on-a-x86-64-machine"]], "Utilities for Developers": [[390, "utilities-for-developers"]], "Validation Tools": [[390, "validation-tools"]], "Efficient Linear Algebra & Array Operations": [[390, "efficient-linear-algebra-array-operations"]], "Efficient Random Sampling": [[390, "efficient-random-sampling"]], "Efficient Routines for Sparse Matrices": [[390, "efficient-routines-for-sparse-matrices"]], "Graph Routines": [[390, "graph-routines"]], "Testing Functions": [[390, "testing-functions"]], "Multiclass and multilabel utility function": [[390, "multiclass-and-multilabel-utility-function"]], "Helper Functions": [[390, "helper-functions"]], "Hash Functions": [[390, "hash-functions"]], "Warnings and Exceptions": [[390, "warnings-and-exceptions"]], "Dispatching": [[391, "dispatching"]], "Frequently Asked Questions": [[393, "frequently-asked-questions"]], "Table of Contents": [[393, "table-of-contents"]], "About the project": [[393, "about-the-project"]], "What is the project name (a lot of people get it wrong)?": [[393, "what-is-the-project-name-a-lot-of-people-get-it-wrong"]], "How do you pronounce the project name?": [[393, "how-do-you-pronounce-the-project-name"]], "Why scikit?": [[393, "why-scikit"]], "Do you support PyPy?": [[393, "do-you-support-pypy"]], "Implementation decisions": [[393, "implementation-decisions"]], "Why is there no support for deep or reinforcement learning / Will there be support for deep or reinforcement learning in scikit-learn?": [[393, "why-is-there-no-support-for-deep-or-reinforcement-learning-will-there-be-support-for-deep-or-reinforcement-learning-in-scikit-learn"]], "Will you add graphical models or sequence prediction to scikit-learn?": [[393, "will-you-add-graphical-models-or-sequence-prediction-to-scikit-learn"]], "Why did you remove HMMs from scikit-learn?": [[393, "why-did-you-remove-hmms-from-scikit-learn"]], "Will you add GPU support?": [[393, "will-you-add-gpu-support"]], "Why do categorical variables need preprocessing in scikit-learn, compared to other tools?": [[393, "why-do-categorical-variables-need-preprocessing-in-scikit-learn-compared-to-other-tools"]], "Why does Scikit-learn not directly work with, for example, pandas.DataFrame?": [[393, "why-does-scikit-learn-not-directly-work-with-for-example-pandas-dataframe"]], "Do you plan to implement transform for target y in a pipeline?": [[393, "do-you-plan-to-implement-transform-for-target-y-in-a-pipeline"]], "Why are there so many different estimators for linear models?": [[393, "why-are-there-so-many-different-estimators-for-linear-models"]], "How can I contribute to scikit-learn?": [[393, "how-can-i-contribute-to-scikit-learn"]], "Why is my pull request not getting any attention?": [[393, "why-is-my-pull-request-not-getting-any-attention"]], "What are the inclusion criteria for new algorithms ?": [[393, "what-are-the-inclusion-criteria-for-new-algorithms"]], "Why are you so selective on what algorithms you include in scikit-learn?": [[393, "why-are-you-so-selective-on-what-algorithms-you-include-in-scikit-learn"]], "Using scikit-learn": [[393, "using-scikit-learn"]], "What\u2019s the best way to get help on scikit-learn usage?": [[393, "what-s-the-best-way-to-get-help-on-scikit-learn-usage"]], "How should I save, export or deploy estimators for production?": [[393, "how-should-i-save-export-or-deploy-estimators-for-production"]], "How can I create a bunch object?": [[393, "how-can-i-create-a-bunch-object"]], "How can I load my own datasets into a format usable by scikit-learn?": [[393, "how-can-i-load-my-own-datasets-into-a-format-usable-by-scikit-learn"]], "How do I deal with string data (or trees, graphs\u2026)?": [[393, "how-do-i-deal-with-string-data-or-trees-graphs"]], "Why do I sometime get a crash/freeze with n_jobs > 1 under OSX or Linux?": [[393, "why-do-i-sometime-get-a-crash-freeze-with-n-jobs-1-under-osx-or-linux"]], "Why does my job use more cores than specified with n_jobs?": [[393, "why-does-my-job-use-more-cores-than-specified-with-n-jobs"]], "How do I set a random_state for an entire execution?": [[393, "how-do-i-set-a-random-state-for-an-entire-execution"]], "Getting Started": [[394, "getting-started"]], "Fitting and predicting: estimator basics": [[394, "fitting-and-predicting-estimator-basics"]], "Transformers and pre-processors": [[394, "transformers-and-pre-processors"]], "Pipelines: chaining pre-processors and estimators": [[394, "pipelines-chaining-pre-processors-and-estimators"]], "Model evaluation": [[394, "model-evaluation"]], "Automatic parameter searches": [[394, "automatic-parameter-searches"]], "Next steps": [[394, "next-steps"]], "Glossary of Common Terms and API Elements": [[395, "glossary-of-common-terms-and-api-elements"]], "General Concepts": [[395, "general-concepts"]], "Class APIs and Estimator Types": [[395, "class-apis-and-estimator-types"]], "Target Types": [[395, "target-types"]], "Methods": [[395, "methods"]], "Parameters": [[395, "parameters"], [415, "parameters"]], "Attributes": [[395, "attributes"]], "Data and sample properties": [[395, "data-and-sample-properties"]], "Scikit-learn governance and decision-making": [[396, "scikit-learn-governance-and-decision-making"]], "Roles And Responsibilities": [[396, "roles-and-responsibilities"]], "Contributors": [[396, "contributors"]], "Core Contributors": [[396, "core-contributors"]], "Communication team": [[396, "communication-team"]], "Documentation team": [[396, "documentation-team"]], "Maintainers": [[396, "maintainers"]], "Technical Committee": [[396, "technical-committee"]], "Decision Making Process": [[396, "decision-making-process"]], "Governance Model Changes": [[396, "governance-model-changes"]], "Enhancement proposals (SLEPs)": [[396, "enhancement-proposals-sleps"]], "Installing scikit-learn": [[398, "installing-scikit-learn"]], "Installing the latest release": [[398, "installing-the-latest-release"]], "Third party distributions of scikit-learn": [[398, "third-party-distributions-of-scikit-learn"]], "Alpine Linux": [[398, "alpine-linux"]], "Arch Linux": [[398, "arch-linux"]], "Debian/Ubuntu": [[398, "debian-ubuntu"]], "Fedora": [[398, "fedora"]], "NetBSD": [[398, "netbsd"]], "MacPorts for Mac OSX": [[398, "macports-for-mac-osx"]], "Anaconda and Enthought Deployment Manager for all supported platforms": [[398, "anaconda-and-enthought-deployment-manager-for-all-supported-platforms"]], "Intel Extension for Scikit-learn": [[398, "intel-extension-for-scikit-learn"]], "WinPython for Windows": [[398, "winpython-for-windows"]], "Troubleshooting": [[398, "troubleshooting"]], "Error caused by file path length limit on Windows": [[398, "error-caused-by-file-path-length-limit-on-windows"]], "Usage Examples": [[399, "usage-examples"]], "Weighted scoring and fitting": [[399, "weighted-scoring-and-fitting"]], "Weighted scoring and unweighted fitting": [[399, "weighted-scoring-and-unweighted-fitting"]], "Unweighted feature selection": [[399, "unweighted-feature-selection"]], "Advanced: Different scoring and fitting weights": [[399, "advanced-different-scoring-and-fitting-weights"]], "API Interface": [[399, "api-interface"]], "Metadata Routing Support Status": [[399, "metadata-routing-support-status"]], "Model persistence": [[402, "model-persistence"]], "Python specific serialization": [[402, "python-specific-serialization"]], "Security & maintainability limitations": [[402, "security-maintainability-limitations"]], "A more secure format: skops": [[402, "a-more-secure-format-skops"]], "Interoperable formats": [[402, "interoperable-formats"]], "Model selection and evaluation": [[403, "model-selection-and-evaluation"]], "Array API support (experimental)": [[404, "array-api-support-experimental"]], "Example usage": [[404, "example-usage"]], "PyTorch Support": [[404, "pytorch-support"]], "Support for Array API-compatible inputs": [[404, "support-for-array-api-compatible-inputs"]], "Metrics": [[404, "metrics"]], "Tools": [[404, "tools"]], "Common estimator checks": [[404, "common-estimator-checks"]], "Note on MPS device support": [[404, "note-on-mps-device-support"]], "Spectral Co-Clustering": [[405, "spectral-co-clustering"]], 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"module-sklearn"]], "sklearn.base: Base classes and utility functions": [[407, "module-sklearn.base"]], "Base classes": [[407, "base-classes"]], "Functions": [[407, "functions"], [407, "id1"]], "sklearn.calibration: Probability Calibration": [[407, "module-sklearn.calibration"]], "sklearn.cluster: Clustering": [[407, "module-sklearn.cluster"]], "Classes": [[407, "classes"]], "sklearn.compose: Composite Estimators": [[407, "module-sklearn.compose"]], "sklearn.covariance: Covariance Estimators": [[407, "module-sklearn.covariance"]], "sklearn.cross_decomposition: Cross decomposition": [[407, "module-sklearn.cross_decomposition"]], "sklearn.datasets: Datasets": [[407, "module-sklearn.datasets"]], "Loaders": [[407, "loaders"]], "Samples generator": [[407, "samples-generator"]], "sklearn.decomposition: Matrix Decomposition": [[407, "module-sklearn.decomposition"]], "sklearn.discriminant_analysis: Discriminant Analysis": [[407, "module-sklearn.discriminant_analysis"]], "sklearn.dummy: Dummy 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Kernel Approximation": [[407, "module-sklearn.kernel_approximation"]], "sklearn.kernel_ridge: Kernel Ridge Regression": [[407, "module-sklearn.kernel_ridge"]], "sklearn.linear_model: Linear Models": [[407, "module-sklearn.linear_model"]], "Linear classifiers": [[407, "linear-classifiers"]], "Classical linear regressors": [[407, "classical-linear-regressors"]], "Regressors with variable selection": [[407, "regressors-with-variable-selection"]], "Bayesian regressors": [[407, "bayesian-regressors"]], "Multi-task linear regressors with variable selection": [[407, "multi-task-linear-regressors-with-variable-selection"]], "Outlier-robust regressors": [[407, "outlier-robust-regressors"]], "Generalized linear models (GLM) for regression": [[407, "generalized-linear-models-glm-for-regression"]], "sklearn.manifold: Manifold Learning": [[407, "module-sklearn.manifold"]], "sklearn.metrics: Metrics": [[407, "sklearn-metrics-metrics"]], "Model Selection Interface": [[407, 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"module-sklearn.utils"]], "Input and parameter validation": [[407, "module-sklearn.utils.validation"]], "Utilities used in meta-estimators": [[407, "module-sklearn.utils.metaestimators"]], "Utilities to handle weights based on class labels": [[407, "module-sklearn.utils.class_weight"]], "Utilities to deal with multiclass target in classifiers": [[407, "module-sklearn.utils.multiclass"]], "Utilities for optimal mathematical operations": [[407, "module-sklearn.utils.extmath"]], "Utilities to work with sparse matrices and arrays": [[407, "module-sklearn.utils.sparsefuncs"]], "Utilities to work with graphs": [[407, "module-sklearn.utils.graph"]], "Utilities for random sampling": [[407, "module-sklearn.utils.random"]], "Utilities to operate on arrays": [[407, "module-sklearn.utils.arrayfuncs"]], "Metadata routing": [[407, "module-sklearn.utils.metadata_routing"]], "Scikit-learn object discovery": [[407, "module-sklearn.utils.discovery"]], "Scikit-learn compatibility checker": [[407, "module-sklearn.utils.estimator_checks"]], "Utilities for parallel computing": [[407, "module-sklearn.utils.parallel"]], "Recently deprecated": [[407, "recently-deprecated"]], "Input data": [[408, null]], "Overview of clustering methods": [[408, "overview-of-clustering-methods"]], "K-means": [[408, "k-means"]], "Low-level parallelism": [[408, "low-level-parallelism"], [415, "low-level-parallelism"]], "Mini Batch K-Means": [[408, "mini-batch-k-means"]], "Affinity Propagation": [[408, "affinity-propagation"]], "Mean Shift": [[408, "mean-shift"]], "Spectral clustering": [[408, "spectral-clustering"]], "Different label assignment strategies": [[408, "different-label-assignment-strategies"]], "Spectral Clustering Graphs": [[408, "spectral-clustering-graphs"]], "Hierarchical clustering": [[408, "hierarchical-clustering"]], "FeatureAgglomeration": [[408, null]], "Different linkage type: Ward, complete, average, and single linkage": [[408, 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"cross-validation-iterators-for-i-i-d-data"]], "K-fold": [[412, "k-fold"]], "Repeated K-Fold": [[412, "repeated-k-fold"]], "Leave One Out (LOO)": [[412, "leave-one-out-loo"]], "Leave P Out (LPO)": [[412, "leave-p-out-lpo"]], "Random permutations cross-validation a.k.a. Shuffle & Split": [[412, "random-permutations-cross-validation-a-k-a-shuffle-split"]], "Cross-validation iterators with stratification based on class labels": [[412, "cross-validation-iterators-with-stratification-based-on-class-labels"]], "Stratified k-fold": [[412, "stratified-k-fold"]], "Stratified Shuffle Split": [[412, "stratified-shuffle-split"]], "Cross-validation iterators for grouped data": [[412, "cross-validation-iterators-for-grouped-data"]], "Group k-fold": [[412, "group-k-fold"]], "StratifiedGroupKFold": [[412, "stratifiedgroupkfold"]], "Leave One Group Out": [[412, "leave-one-group-out"]], "Leave P Groups Out": [[412, "leave-p-groups-out"]], "Group Shuffle Split": [[412, "group-shuffle-split"]], "Predefined fold-splits / Validation-sets": [[412, "predefined-fold-splits-validation-sets"]], "Using cross-validation iterators to split train and test": [[412, "using-cross-validation-iterators-to-split-train-and-test"]], "Cross validation of time series data": [[412, "cross-validation-of-time-series-data"]], "Time Series Split": [[412, "time-series-split"]], "A note on shuffling": [[412, "a-note-on-shuffling"]], "Cross validation and model selection": [[412, "cross-validation-and-model-selection"]], "Decomposing signals in components (matrix factorization problems)": [[413, "decomposing-signals-in-components-matrix-factorization-problems"]], "Principal component analysis (PCA)": [[413, "principal-component-analysis-pca"]], "Exact PCA and probabilistic interpretation": [[413, "exact-pca-and-probabilistic-interpretation"]], "PCA using randomized SVD": [[413, "pca-using-randomized-svd"]], "Sparse principal components analysis (SparsePCA and MiniBatchSparsePCA)": [[413, "sparse-principal-components-analysis-sparsepca-and-minibatchsparsepca"]], "Kernel Principal Component Analysis (kPCA)": [[413, "kernel-principal-component-analysis-kpca"]], "Exact Kernel PCA": [[413, "exact-kernel-pca"]], "Choice of solver for Kernel PCA": [[413, "choice-of-solver-for-kernel-pca"]], "Truncated singular value decomposition and latent semantic analysis": [[413, "truncated-singular-value-decomposition-and-latent-semantic-analysis"]], "Dictionary Learning": [[413, "dictionary-learning"]], "Generic dictionary learning": [[413, "generic-dictionary-learning"]], "Mini-batch dictionary learning": [[413, "mini-batch-dictionary-learning"]], "Clustering for dictionary learning": [[413, null]], "Factor Analysis": [[413, "factor-analysis"]], "Independent component analysis (ICA)": [[413, "independent-component-analysis-ica"]], "Non-negative matrix factorization (NMF or NNMF)": [[413, "non-negative-matrix-factorization-nmf-or-nnmf"]], "NMF with the Frobenius norm": [[413, "nmf-with-the-frobenius-norm"]], "NMF with a beta-divergence": [[413, "nmf-with-a-beta-divergence"]], "Mini-batch Non Negative Matrix Factorization": [[413, "mini-batch-non-negative-matrix-factorization"]], "Latent Dirichlet Allocation (LDA)": [[413, 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[[642, "sklearn-kernel-ridge-kernelridge"]], "Examples using sklearn.kernel_ridge.KernelRidge": [[642, "examples-using-sklearn-kernel-ridge-kernelridge"]], "sklearn.linear_model.ARDRegression": [[643, "sklearn-linear-model-ardregression"]], "Examples using sklearn.linear_model.ARDRegression": [[643, "examples-using-sklearn-linear-model-ardregression"]], "sklearn.linear_model.BayesianRidge": [[644, "sklearn-linear-model-bayesianridge"]], "Examples using sklearn.linear_model.BayesianRidge": [[644, "examples-using-sklearn-linear-model-bayesianridge"]], "sklearn.linear_model.ElasticNet": [[645, "sklearn-linear-model-elasticnet"]], "Examples using sklearn.linear_model.ElasticNet": [[645, "examples-using-sklearn-linear-model-elasticnet"]], "sklearn.linear_model.ElasticNetCV": [[646, "sklearn-linear-model-elasticnetcv"]], "Examples using sklearn.linear_model.ElasticNetCV": [[646, "examples-using-sklearn-linear-model-elasticnetcv"]], "sklearn.linear_model.GammaRegressor": [[647, 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"Examples using sklearn.metrics.root_mean_squared_error": [[788, "examples-using-sklearn-metrics-root-mean-squared-error"]], "sklearn.metrics.root_mean_squared_log_error": [[789, "sklearn-metrics-root-mean-squared-log-error"]], "sklearn.metrics.silhouette_samples": [[790, "sklearn-metrics-silhouette-samples"]], "Examples using sklearn.metrics.silhouette_samples": [[790, "examples-using-sklearn-metrics-silhouette-samples"]], "sklearn.metrics.silhouette_score": [[791, "sklearn-metrics-silhouette-score"]], "Examples using sklearn.metrics.silhouette_score": [[791, "examples-using-sklearn-metrics-silhouette-score"]], "sklearn.metrics.top_k_accuracy_score": [[792, "sklearn-metrics-top-k-accuracy-score"]], "sklearn.metrics.v_measure_score": [[793, "sklearn-metrics-v-measure-score"]], "Examples using sklearn.metrics.v_measure_score": [[793, "examples-using-sklearn-metrics-v-measure-score"]], "sklearn.metrics.zero_one_loss": [[794, "sklearn-metrics-zero-one-loss"]], "sklearn.mixture.BayesianGaussianMixture": [[795, "sklearn-mixture-bayesiangaussianmixture"]], "Examples using sklearn.mixture.BayesianGaussianMixture": [[795, "examples-using-sklearn-mixture-bayesiangaussianmixture"]], "sklearn.mixture.GaussianMixture": [[796, "sklearn-mixture-gaussianmixture"]], "Examples using sklearn.mixture.GaussianMixture": [[796, "examples-using-sklearn-mixture-gaussianmixture"]], "sklearn.model_selection.GridSearchCV": [[797, "sklearn-model-selection-gridsearchcv"]], "Examples using sklearn.model_selection.GridSearchCV": [[797, "examples-using-sklearn-model-selection-gridsearchcv"]], "sklearn.model_selection.GroupKFold": [[798, "sklearn-model-selection-groupkfold"]], "Examples using sklearn.model_selection.GroupKFold": [[798, "examples-using-sklearn-model-selection-groupkfold"]], "sklearn.model_selection.GroupShuffleSplit": [[799, "sklearn-model-selection-groupshufflesplit"]], "Examples using sklearn.model_selection.GroupShuffleSplit": [[799, 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"sklearn.utils.metadata_routing.MetadataRouter": [[945, "sklearn-utils-metadata-routing-metadatarouter"]], "Examples using sklearn.utils.metadata_routing.MetadataRouter": [[945, "examples-using-sklearn-utils-metadata-routing-metadatarouter"]], "sklearn.utils.metadata_routing.MethodMapping": [[946, "sklearn-utils-metadata-routing-methodmapping"]], "Examples using sklearn.utils.metadata_routing.MethodMapping": [[946, "examples-using-sklearn-utils-metadata-routing-methodmapping"]], "sklearn.utils.metadata_routing.get_routing_for_object": [[947, "sklearn-utils-metadata-routing-get-routing-for-object"]], "Examples using sklearn.utils.metadata_routing.get_routing_for_object": [[947, "examples-using-sklearn-utils-metadata-routing-get-routing-for-object"]], "sklearn.utils.metadata_routing.process_routing": [[948, "sklearn-utils-metadata-routing-process-routing"]], "Examples using sklearn.utils.metadata_routing.process_routing": [[948, 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"sklearn.utils.register_parallel_backend": [[958, "sklearn-utils-register-parallel-backend"]], "sklearn.utils.resample": [[959, "sklearn-utils-resample"]], "sklearn.utils.safe_mask": [[960, "sklearn-utils-safe-mask"]], "sklearn.utils.safe_sqr": [[961, "sklearn-utils-safe-sqr"]], "sklearn.utils.shuffle": [[962, "sklearn-utils-shuffle"]], "Examples using sklearn.utils.shuffle": [[962, "examples-using-sklearn-utils-shuffle"]], "sklearn.utils.sparsefuncs.incr_mean_variance_axis": [[963, "sklearn-utils-sparsefuncs-incr-mean-variance-axis"]], "sklearn.utils.sparsefuncs.inplace_column_scale": [[964, "sklearn-utils-sparsefuncs-inplace-column-scale"]], "sklearn.utils.sparsefuncs.inplace_csr_column_scale": [[965, "sklearn-utils-sparsefuncs-inplace-csr-column-scale"]], "sklearn.utils.sparsefuncs.inplace_row_scale": [[966, "sklearn-utils-sparsefuncs-inplace-row-scale"]], "sklearn.utils.sparsefuncs.inplace_swap_column": [[967, "sklearn-utils-sparsefuncs-inplace-swap-column"]], "sklearn.utils.sparsefuncs.inplace_swap_row": [[968, "sklearn-utils-sparsefuncs-inplace-swap-row"]], "sklearn.utils.sparsefuncs.mean_variance_axis": [[969, "sklearn-utils-sparsefuncs-mean-variance-axis"]], "sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l1": [[970, "sklearn-utils-sparsefuncs-fast-inplace-csr-row-normalize-l1"]], "sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l2": [[971, "sklearn-utils-sparsefuncs-fast-inplace-csr-row-normalize-l2"]], "sklearn.utils.validation.check_is_fitted": [[972, "sklearn-utils-validation-check-is-fitted"]], "Examples using sklearn.utils.validation.check_is_fitted": [[972, "examples-using-sklearn-utils-validation-check-is-fitted"]], "sklearn.utils.validation.check_memory": [[973, "sklearn-utils-validation-check-memory"]], "sklearn.utils.validation.check_symmetric": [[974, "sklearn-utils-validation-check-symmetric"]], "sklearn.utils.validation.column_or_1d": [[975, "sklearn-utils-validation-column-or-1d"]], "sklearn.utils.validation.has_fit_parameter": [[976, "sklearn-utils-validation-has-fit-parameter"]], "Tuning the hyper-parameters of an estimator": [[977, "tuning-the-hyper-parameters-of-an-estimator"]], "Exhaustive Grid Search": [[977, "exhaustive-grid-search"]], "Randomized Parameter Optimization": [[977, "randomized-parameter-optimization"]], "Searching for optimal parameters with successive halving": [[977, "searching-for-optimal-parameters-with-successive-halving"]], "Choosing min_resources and the number of candidates": [[977, "choosing-min-resources-and-the-number-of-candidates"]], "Amount of resource and number of candidates at each iteration": [[977, "amount-of-resource-and-number-of-candidates-at-each-iteration"]], "Choosing a resource": [[977, "choosing-a-resource"]], "Exhausting the available resources": [[977, "exhausting-the-available-resources"]], "Aggressive elimination of candidates": [[977, "aggressive-elimination-of-candidates"]], "Analyzing results with the cv_results_ attribute": [[977, "analyzing-results-with-the-cv-results-attribute"]], "Tips for parameter search": [[977, "tips-for-parameter-search"]], "Specifying an objective metric": [[977, "specifying-an-objective-metric"]], "Specifying multiple metrics for evaluation": [[977, "specifying-multiple-metrics-for-evaluation"]], "Composite estimators and parameter spaces": [[977, "composite-estimators-and-parameter-spaces"]], "Model selection: development and evaluation": [[977, "model-selection-development-and-evaluation"]], "Robustness to failure": [[977, "robustness-to-failure"]], "Alternatives to brute force parameter search": [[977, "alternatives-to-brute-force-parameter-search"]], "Model specific cross-validation": [[977, "model-specific-cross-validation"]], "Information Criterion": [[977, "information-criterion"]], "Out of Bag Estimates": [[977, "out-of-bag-estimates"]], "Imputation of missing values": [[978, "imputation-of-missing-values"], [998, "imputation-of-missing-values"]], "Univariate vs. Multivariate Imputation": [[978, "univariate-vs-multivariate-imputation"]], "Univariate feature imputation": [[978, "univariate-feature-imputation"]], "Multivariate feature imputation": [[978, "multivariate-feature-imputation"]], "Flexibility of IterativeImputer": [[978, "flexibility-of-iterativeimputer"]], "Multiple vs. Single Imputation": [[978, "multiple-vs-single-imputation"]], "Nearest neighbors imputation": [[978, "nearest-neighbors-imputation"]], "Keeping the number of features constant": [[978, "keeping-the-number-of-features-constant"]], "Marking imputed values": [[978, "marking-imputed-values"]], "Estimators that handle NaN values": [[978, "estimators-that-handle-nan-values"]], "Isotonic regression": [[979, "isotonic-regression"]], "Nystroem Method for Kernel Approximation": [[980, "nystroem-method-for-kernel-approximation"]], "Radial Basis Function Kernel": [[980, "radial-basis-function-kernel"]], "Additive Chi Squared Kernel": [[980, "additive-chi-squared-kernel"]], "Skewed Chi Squared Kernel": [[980, "skewed-chi-squared-kernel"]], "Polynomial Kernel Approximation via Tensor Sketch": [[980, "polynomial-kernel-approximation-via-tensor-sketch"]], "Mathematical Details": [[980, "mathematical-details"]], "Kernel ridge regression": [[981, "kernel-ridge-regression"]], "Linear and Quadratic Discriminant Analysis": [[982, "linear-and-quadratic-discriminant-analysis"]], "Dimensionality reduction using Linear Discriminant Analysis": [[982, "dimensionality-reduction-using-linear-discriminant-analysis"]], "Mathematical formulation of the LDA and QDA classifiers": [[982, "mathematical-formulation-of-the-lda-and-qda-classifiers"]], "QDA": [[982, "qda"]], "LDA": [[982, "lda"]], "Mathematical formulation of LDA dimensionality reduction": [[982, "mathematical-formulation-of-lda-dimensionality-reduction"]], "Shrinkage and Covariance Estimator": [[982, "shrinkage-and-covariance-estimator"]], "Estimation algorithms": [[982, "estimation-algorithms"]], "Validation curves: plotting scores to evaluate models": [[983, "validation-curves-plotting-scores-to-evaluate-models"]], "Validation curve": [[983, "validation-curve"]], "Learning curve": [[983, "learning-curve"]], "Linear Models": [[984, "linear-models"]], "Ordinary Least Squares": [[984, "ordinary-least-squares"]], "Non-Negative Least Squares": [[984, "non-negative-least-squares"]], "Ordinary Least Squares Complexity": [[984, "ordinary-least-squares-complexity"]], "Ridge regression and classification": [[984, "ridge-regression-and-classification"]], "Ridge Complexity": [[984, "ridge-complexity"]], "Setting the regularization parameter: leave-one-out Cross-Validation": [[984, "setting-the-regularization-parameter-leave-one-out-cross-validation"]], "Setting regularization parameter": [[984, "setting-regularization-parameter"]], "Using cross-validation": [[984, "using-cross-validation"]], "Information-criteria based model selection": [[984, "information-criteria-based-model-selection"]], "AIC and BIC criteria": [[984, "aic-and-bic-criteria"]], "Comparison with the regularization parameter of SVM": [[984, "comparison-with-the-regularization-parameter-of-svm"]], "Multi-task Lasso": [[984, "multi-task-lasso"]], "Elastic-Net": [[984, "elastic-net"]], "Multi-task Elastic-Net": [[984, "multi-task-elastic-net"]], "Least Angle Regression": [[984, "least-angle-regression"]], "LARS Lasso": [[984, "lars-lasso"]], "Orthogonal Matching Pursuit (OMP)": [[984, "orthogonal-matching-pursuit-omp"]], "Bayesian Regression": [[984, "bayesian-regression"]], "Bayesian Ridge Regression": [[984, "bayesian-ridge-regression"]], "Automatic Relevance Determination - ARD": [[984, "automatic-relevance-determination-ard"]], "Logistic regression": [[984, "logistic-regression"]], "Binary Case": [[984, "binary-case"]], "Multinomial Case": [[984, "multinomial-case"]], "Solvers": [[984, "solvers"]], "Differences between solvers": [[984, "differences-between-solvers"]], "Stochastic Gradient Descent - SGD": [[984, "stochastic-gradient-descent-sgd"]], "Perceptron": [[984, "perceptron"]], "Passive Aggressive Algorithms": [[984, "passive-aggressive-algorithms"]], "Robustness regression: outliers and modeling errors": [[984, "robustness-regression-outliers-and-modeling-errors"]], "Different scenario and useful concepts": [[984, "different-scenario-and-useful-concepts"]], "Trade-offs: which estimator ?": [[984, null]], "RANSAC: RANdom SAmple Consensus": [[984, "ransac-random-sample-consensus"]], "Theil-Sen estimator: generalized-median-based estimator": [[984, "theil-sen-estimator-generalized-median-based-estimator"]], "Huber Regression": [[984, "huber-regression"]], "Quantile Regression": [[984, "quantile-regression"]], "Polynomial regression: extending linear models with basis functions": [[984, "polynomial-regression-extending-linear-models-with-basis-functions"]], "Introduction": [[985, "introduction"]], "Isomap": [[985, "isomap"]], "Locally Linear Embedding": [[985, "locally-linear-embedding"]], "Modified Locally Linear Embedding": [[985, "modified-locally-linear-embedding"]], "Hessian Eigenmapping": [[985, "hessian-eigenmapping"]], "Spectral Embedding": [[985, "spectral-embedding"]], "Local Tangent Space Alignment": [[985, "local-tangent-space-alignment"]], "Multi-dimensional Scaling (MDS)": [[985, "multi-dimensional-scaling-mds"]], "t-distributed Stochastic Neighbor Embedding (t-SNE)": [[985, "t-distributed-stochastic-neighbor-embedding-t-sne"]], "Tips on practical use": [[985, "tips-on-practical-use"], [1004, "tips-on-practical-use"]], "Pairwise metrics, Affinities and Kernels": [[986, "pairwise-metrics-affinities-and-kernels"]], "Cosine similarity": [[986, "cosine-similarity"]], "Laplacian kernel": [[986, "laplacian-kernel"]], "Chi-squared kernel": [[986, "chi-squared-kernel"]], "Gaussian mixture models": [[987, "gaussian-mixture-models"]], "Gaussian Mixture": [[987, "gaussian-mixture"]], "Pros:": [[987, null], [987, null]], "Cons:": [[987, null], [987, null]], "Variational Bayesian Gaussian Mixture": [[987, "variational-bayesian-gaussian-mixture"]], "The Dirichlet Process": [[987, "the-dirichlet-process"]], "Metrics and scoring: quantifying the quality of predictions": [[988, "metrics-and-scoring-quantifying-the-quality-of-predictions"]], "The scoring parameter: defining model evaluation rules": [[988, "the-scoring-parameter-defining-model-evaluation-rules"]], "Common cases: predefined values": [[988, "common-cases-predefined-values"]], "Defining your scoring strategy from metric functions": [[988, "defining-your-scoring-strategy-from-metric-functions"]], "Implementing your own scoring object": [[988, "implementing-your-own-scoring-object"]], "Using multiple metric evaluation": [[988, "using-multiple-metric-evaluation"]], "From binary to multiclass and multilabel": [[988, "from-binary-to-multiclass-and-multilabel"]], "Accuracy score": [[988, "accuracy-score"]], "Example:": 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"receiver-operating-characteristic-roc"]], "Binary case": [[988, "binary-case"]], "Multi-class case": [[988, "multi-class-case"]], "Multi-label case": [[988, "multi-label-case"]], "Detection error tradeoff (DET)": [[988, "detection-error-tradeoff-det"]], "Zero one loss": [[988, "zero-one-loss"]], "Brier score loss": [[988, "brier-score-loss"]], "Class likelihood ratios": [[988, "class-likelihood-ratios"]], "Coverage error": [[988, "coverage-error"]], "Label ranking average precision": [[988, "label-ranking-average-precision"]], "Ranking loss": [[988, "ranking-loss"]], "Normalized Discounted Cumulative Gain": [[988, "normalized-discounted-cumulative-gain"]], "R\u00b2 score, the coefficient of determination": [[988, "r2-score-the-coefficient-of-determination"]], "Mean absolute error": [[988, "mean-absolute-error"]], "Mean squared error": [[988, "mean-squared-error"]], "Mean squared logarithmic error": [[988, "mean-squared-logarithmic-error"]], "Mean absolute percentage error": [[988, "mean-absolute-percentage-error"]], "Median absolute error": [[988, "median-absolute-error"]], "Max error": [[988, "max-error"]], "Explained variance score": [[988, "explained-variance-score"]], "Link to R\u00b2 score, the coefficient of determination": [[988, null]], "Mean Poisson, Gamma, and Tweedie deviances": [[988, "mean-poisson-gamma-and-tweedie-deviances"]], "Pinball loss": [[988, "pinball-loss"]], "D\u00b2 score": [[988, "d2-score"]], "D\u00b2 Tweedie score": [[988, "d2-tweedie-score"]], "D\u00b2 pinball score": [[988, "d2-pinball-score"]], "D\u00b2 absolute error score": [[988, "d2-absolute-error-score"]], "Visual evaluation of regression models": [[988, "visual-evaluation-of-regression-models"]], "Dummy estimators": [[988, "dummy-estimators"]], "Multiclass and multioutput algorithms": [[989, "multiclass-and-multioutput-algorithms"]], "Multiclass classification": [[989, "multiclass-classification"], [1022, null]], "Target format": [[989, "target-format"], [989, "id5"], [989, "id8"], [989, "id10"]], "OneVsRestClassifier": [[989, "onevsrestclassifier"]], "OneVsOneClassifier": [[989, "onevsoneclassifier"]], "OutputCodeClassifier": [[989, "outputcodeclassifier"]], "MultiOutputClassifier": [[989, "multioutputclassifier"]], "ClassifierChain": [[989, "classifierchain"]], "Multiclass-multioutput classification": [[989, "multiclass-multioutput-classification"]], "Multioutput regression": [[989, "multioutput-regression"]], "MultiOutputRegressor": [[989, "multioutputregressor"]], "RegressorChain": [[989, "regressorchain"]], "Naive Bayes": [[990, "naive-bayes"]], "Multinomial Naive Bayes": [[990, "multinomial-naive-bayes"]], "Complement Naive Bayes": [[990, "complement-naive-bayes"]], "Bernoulli Naive Bayes": [[990, "bernoulli-naive-bayes"]], "Categorical Naive Bayes": [[990, "categorical-naive-bayes"]], "Out-of-core naive Bayes model fitting": [[990, "out-of-core-naive-bayes-model-fitting"]], "Unsupervised Nearest Neighbors": [[991, "unsupervised-nearest-neighbors"]], "Finding the Nearest Neighbors": [[991, "finding-the-nearest-neighbors"]], "KDTree and BallTree Classes": [[991, "kdtree-and-balltree-classes"]], "Nearest Neighbors Regression": [[991, "nearest-neighbors-regression"]], "Nearest Neighbor Algorithms": [[991, "nearest-neighbor-algorithms"]], "Brute Force": [[991, "brute-force"]], "K-D Tree": [[991, "k-d-tree"]], "Ball Tree": [[991, "ball-tree"]], "Choice of Nearest Neighbors Algorithm": [[991, "choice-of-nearest-neighbors-algorithm"]], "Effect of leaf_size": [[991, "effect-of-leaf-size"]], "Valid Metrics for Nearest Neighbor Algorithms": [[991, "valid-metrics-for-nearest-neighbor-algorithms"]], "Nearest Centroid Classifier": [[991, "nearest-centroid-classifier"]], "Nearest Shrunken Centroid": [[991, "nearest-shrunken-centroid"]], "Nearest Neighbors Transformer": [[991, "nearest-neighbors-transformer"]], "Neighborhood Components Analysis": [[991, "neighborhood-components-analysis"]], "Dimensionality reduction": [[991, "dimensionality-reduction"]], "Mahalanobis distance": [[991, "mahalanobis-distance"]], "Complexity": [[991, "complexity"], [992, "complexity"], [1002, "complexity"], [1003, "complexity"], [1004, "complexity"]], "Transform": [[991, "transform"]], "Neural network models (supervised)": [[992, "neural-network-models-supervised"]], "Multi-layer Perceptron": [[992, "multi-layer-perceptron"]], "Regularization": [[992, "regularization"]], "Algorithms": [[992, "algorithms"]], "Tips on Practical Use": [[992, "tips-on-practical-use"], [1002, "tips-on-practical-use"], [1003, "tips-on-practical-use"]], "More control with warm_start": [[992, "more-control-with-warm-start"]], "Neural network models (unsupervised)": [[993, "neural-network-models-unsupervised"]], "Restricted Boltzmann machines": [[993, "restricted-boltzmann-machines"]], "Graphical model and parametrization": [[993, "graphical-model-and-parametrization"]], "Bernoulli Restricted Boltzmann machines": [[993, "bernoulli-restricted-boltzmann-machines"]], "Stochastic Maximum Likelihood learning": [[993, "stochastic-maximum-likelihood-learning"]], "Novelty and Outlier Detection": [[994, "novelty-and-outlier-detection"]], "Overview of outlier detection methods": [[994, "overview-of-outlier-detection-methods"]], "Novelty Detection": [[994, "novelty-detection"]], "Scaling up the One-Class SVM": [[994, "scaling-up-the-one-class-svm"]], "Outlier Detection": [[994, "id1"]], "Fitting an elliptic envelope": [[994, "fitting-an-elliptic-envelope"]], "Isolation Forest": [[994, "isolation-forest"]], "Local Outlier Factor": [[994, "local-outlier-factor"]], "Novelty detection with Local Outlier Factor": [[994, "novelty-detection-with-local-outlier-factor"]], "Partial Dependence and Individual Conditional Expectation plots": [[995, "partial-dependence-and-individual-conditional-expectation-plots"]], "Partial dependence plots": [[995, "partial-dependence-plots"]], "Individual conditional expectation (ICE) plot": [[995, "individual-conditional-expectation-ice-plot"]], "Mathematical Definition": [[995, "mathematical-definition"]], "Computation methods": [[995, "computation-methods"]], "Permutation feature importance": [[996, "permutation-feature-importance"]], "Outline of the permutation importance algorithm": [[996, "outline-of-the-permutation-importance-algorithm"]], "Relation to impurity-based importance in trees": [[996, "relation-to-impurity-based-importance-in-trees"]], "Misleading values on strongly correlated features": [[996, "misleading-values-on-strongly-correlated-features"]], "Preprocessing data": [[998, "preprocessing-data"]], "Standardization, or mean removal and variance scaling": [[998, "standardization-or-mean-removal-and-variance-scaling"]], "Scaling features to a range": [[998, "scaling-features-to-a-range"]], "Scaling sparse data": [[998, "scaling-sparse-data"]], "Scaling data with outliers": [[998, "scaling-data-with-outliers"]], "Scaling vs Whitening": [[998, null]], "Centering kernel matrices": [[998, "centering-kernel-matrices"]], "Non-linear transformation": [[998, "non-linear-transformation"]], "Mapping to a Uniform distribution": [[998, "mapping-to-a-uniform-distribution"]], "Mapping to a Gaussian distribution": [[998, "mapping-to-a-gaussian-distribution"]], "Normalization": [[998, "normalization"]], "Sparse input": [[998, null], [998, null]], "Encoding categorical features": [[998, "encoding-categorical-features"]], "Infrequent categories": [[998, "infrequent-categories"]], "Target Encoder": [[998, "target-encoder"]], "Discretization": [[998, "discretization"]], "K-bins discretization": [[998, "k-bins-discretization"]], "Feature binarization": [[998, "feature-binarization"]], "Generating polynomial features": [[998, "generating-polynomial-features"]], "Polynomial features": [[998, "polynomial-features"]], "Spline transformer": [[998, "spline-transformer"]], "Custom transformers": [[998, "custom-transformers"]], "Transforming the prediction target (y)": [[999, "transforming-the-prediction-target-y"]], "Label binarization": [[999, "label-binarization"]], "LabelBinarizer": [[999, "labelbinarizer"]], "MultiLabelBinarizer": [[999, "multilabelbinarizer"]], "Label encoding": [[999, "label-encoding"]], "Random Projection": [[1000, "random-projection"]], "The Johnson-Lindenstrauss lemma": [[1000, "the-johnson-lindenstrauss-lemma"]], "Gaussian random projection": [[1000, "gaussian-random-projection"]], "Sparse random projection": [[1000, "sparse-random-projection"]], "Inverse Transform": [[1000, "inverse-transform"]], "Unlabeled entries in y": [[1001, null]], "Self Training": [[1001, "self-training"]], "Label Propagation": [[1001, "label-propagation"]], "Stochastic Gradient Descent": [[1002, "stochastic-gradient-descent"]], "Stochastic Gradient Descent for sparse data": [[1002, "stochastic-gradient-descent-for-sparse-data"]], "Stopping criterion": [[1002, "stopping-criterion"]], "SGD": [[1002, "id5"]], "Implementation details": [[1002, "implementation-details"], [1003, "implementation-details"]], "Multi-class classification": [[1003, "multi-class-classification"]], "Scores and probabilities": [[1003, "scores-and-probabilities"]], "Unbalanced problems": [[1003, "unbalanced-problems"]], "Density estimation, novelty detection": [[1003, "density-estimation-novelty-detection"]], "Kernel functions": [[1003, "kernel-functions"]], "Parameters of the RBF Kernel": [[1003, "parameters-of-the-rbf-kernel"]], "Custom Kernels": [[1003, "custom-kernels"]], "SVC": [[1003, "svc"]], "SVR": [[1003, "svr"]], "Multi-output problems": [[1004, "multi-output-problems"]], "Tree algorithms: ID3, C4.5, C5.0 and CART": [[1004, "tree-algorithms-id3-c4-5-c5-0-and-cart"]], "Classification criteria": [[1004, "classification-criteria"]], "Regression criteria": [[1004, "regression-criteria"]], "Missing Values Support": [[1004, "missing-values-support"]], "Minimal Cost-Complexity Pruning": [[1004, "minimal-cost-complexity-pruning"]], "Unsupervised dimensionality reduction": [[1005, "unsupervised-dimensionality-reduction"]], "Pipelining": [[1005, null], [1020, "pipelining"]], "PCA: principal component analysis": [[1005, "pca-principal-component-analysis"]], "Random projections": [[1005, "random-projections"]], "Feature scaling": [[1005, null]], "Welcome to scikit-learn": [[1006, "welcome-to-scikit-learn"]], "External Resources, Videos and Talks": [[1007, "external-resources-videos-and-talks"]], "New to Scientific Python?": [[1007, "new-to-scientific-python"]], "External Tutorials": [[1007, "external-tutorials"]], "Videos": [[1007, "videos"]], "Related Projects": [[1008, "related-projects"]], "Interoperability and framework enhancements": [[1008, "interoperability-and-framework-enhancements"]], "Other estimators and tasks": [[1008, "other-estimators-and-tasks"]], "Statistical learning with Python": [[1008, "statistical-learning-with-python"]], "Recommendation Engine packages": [[1008, 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"mean_absolute_percentage_error() (in module sklearn.metrics)": [[744, "sklearn.metrics.mean_absolute_percentage_error"]], "mean_gamma_deviance() (in module sklearn.metrics)": [[745, "sklearn.metrics.mean_gamma_deviance"]], "mean_pinball_loss() (in module sklearn.metrics)": [[746, "sklearn.metrics.mean_pinball_loss"]], "mean_poisson_deviance() (in module sklearn.metrics)": [[747, "sklearn.metrics.mean_poisson_deviance"]], "mean_squared_error() (in module sklearn.metrics)": [[748, "sklearn.metrics.mean_squared_error"]], "mean_squared_log_error() (in module sklearn.metrics)": [[749, "sklearn.metrics.mean_squared_log_error"]], "mean_tweedie_deviance() (in module sklearn.metrics)": [[750, "sklearn.metrics.mean_tweedie_deviance"]], "median_absolute_error() (in module sklearn.metrics)": [[751, "sklearn.metrics.median_absolute_error"]], "multilabel_confusion_matrix() (in module sklearn.metrics)": [[752, "sklearn.metrics.multilabel_confusion_matrix"]], "mutual_info_score() (in module 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"check_consistent_length() (in module sklearn.utils)": [[922, "sklearn.utils.check_consistent_length"]], "check_random_state() (in module sklearn.utils)": [[923, "sklearn.utils.check_random_state"]], "check_scalar() (in module sklearn.utils)": [[924, "sklearn.utils.check_scalar"]], "compute_class_weight() (in module sklearn.utils.class_weight)": [[925, "sklearn.utils.class_weight.compute_class_weight"]], "compute_sample_weight() (in module sklearn.utils.class_weight)": [[926, "sklearn.utils.class_weight.compute_sample_weight"]], "deprecated() (in module sklearn.utils)": [[927, "sklearn.utils.deprecated"]], "all_displays() (in module sklearn.utils.discovery)": [[928, "sklearn.utils.discovery.all_displays"]], "all_estimators() (in module sklearn.utils.discovery)": [[929, "sklearn.utils.discovery.all_estimators"]], "all_functions() (in module sklearn.utils.discovery)": [[930, "sklearn.utils.discovery.all_functions"]], "check_estimator() (in module sklearn.utils.estimator_checks)": [[931, "sklearn.utils.estimator_checks.check_estimator"]], "parametrize_with_checks() (in module sklearn.utils.estimator_checks)": [[932, "sklearn.utils.estimator_checks.parametrize_with_checks"]], "estimator_html_repr() (in module sklearn.utils)": [[933, "sklearn.utils.estimator_html_repr"]], "density() (in module sklearn.utils.extmath)": [[934, "sklearn.utils.extmath.density"]], "fast_logdet() (in module sklearn.utils.extmath)": [[935, "sklearn.utils.extmath.fast_logdet"]], "randomized_range_finder() (in module sklearn.utils.extmath)": [[936, "sklearn.utils.extmath.randomized_range_finder"]], "randomized_svd() (in module sklearn.utils.extmath)": [[937, "sklearn.utils.extmath.randomized_svd"]], "safe_sparse_dot() (in module sklearn.utils.extmath)": [[938, "sklearn.utils.extmath.safe_sparse_dot"]], "weighted_mode() (in module sklearn.utils.extmath)": [[939, "sklearn.utils.extmath.weighted_mode"]], "gen_batches() (in module sklearn.utils)": [[940, "sklearn.utils.gen_batches"]], "gen_even_slices() (in module sklearn.utils)": [[941, "sklearn.utils.gen_even_slices"]], "single_source_shortest_path_length() (in module sklearn.utils.graph)": [[942, "sklearn.utils.graph.single_source_shortest_path_length"]], "indexable() (in module sklearn.utils)": [[943, "sklearn.utils.indexable"]], "metadatarequest (class in sklearn.utils.metadata_routing)": [[944, "sklearn.utils.metadata_routing.MetadataRequest"]], "consumes() (sklearn.utils.metadata_routing.metadatarequest method)": [[944, "sklearn.utils.metadata_routing.MetadataRequest.consumes"]], "metadatarouter (class in sklearn.utils.metadata_routing)": [[945, "sklearn.utils.metadata_routing.MetadataRouter"]], "add() (sklearn.utils.metadata_routing.metadatarouter method)": [[945, "sklearn.utils.metadata_routing.MetadataRouter.add"]], "add_self_request() (sklearn.utils.metadata_routing.metadatarouter method)": [[945, "sklearn.utils.metadata_routing.MetadataRouter.add_self_request"]], "consumes() 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method)": [[954, "sklearn.utils.parallel.Parallel.format"]], "print_progress() (sklearn.utils.parallel.parallel method)": [[954, "sklearn.utils.parallel.Parallel.print_progress"]], "delayed() (in module sklearn.utils.parallel)": [[955, "sklearn.utils.parallel.delayed"]], "parallel_backend() (in module sklearn.utils)": [[956, "sklearn.utils.parallel_backend"]], "sample_without_replacement() (in module sklearn.utils.random)": [[957, "sklearn.utils.random.sample_without_replacement"]], "register_parallel_backend() (in module sklearn.utils)": [[958, "sklearn.utils.register_parallel_backend"]], "resample() (in module sklearn.utils)": [[959, "sklearn.utils.resample"]], "safe_mask() (in module sklearn.utils)": [[960, "sklearn.utils.safe_mask"]], "safe_sqr() (in module sklearn.utils)": [[961, "sklearn.utils.safe_sqr"]], "shuffle() (in module sklearn.utils)": [[962, "sklearn.utils.shuffle"]], "incr_mean_variance_axis() (in module sklearn.utils.sparsefuncs)": [[963, 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