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<li class="toctree-l1 has-children"><a class="reference internal" href="supervised_learning.html">1. Supervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html">1.1. Linear Models</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/lda_qda.html">1.2. Linear and Quadratic Discriminant Analysis</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_ridge.html">1.3. Kernel ridge regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/svm.html">1.4. Support Vector Machines</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html">1.5. Stochastic Gradient Descent</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neighbors.html">1.6. Nearest Neighbors</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/gaussian_process.html">1.7. Gaussian Processes</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/cross_decomposition.html">1.8. Cross decomposition</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/naive_bayes.html">1.9. Naive Bayes</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/tree.html">1.10. Decision Trees</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/ensemble.html">1.11. Ensembles: Gradient boosting, random forests, bagging, voting, stacking</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/multiclass.html">1.12. Multiclass and multioutput algorithms</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_selection.html">1.13. Feature selection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/semi_supervised.html">1.14. Semi-supervised learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/isotonic.html">1.15. Isotonic regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/calibration.html">1.16. Probability calibration</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_supervised.html">1.17. Neural network models (supervised)</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="unsupervised_learning.html">2. Unsupervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/mixture.html">2.1. Gaussian mixture models</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/manifold.html">2.2. Manifold learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/clustering.html">2.3. Clustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/biclustering.html">2.4. Biclustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/decomposition.html">2.5. Decomposing signals in components (matrix factorization problems)</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/covariance.html">2.6. Covariance estimation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/outlier_detection.html">2.7. Novelty and Outlier Detection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/density.html">2.8. Density Estimation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_unsupervised.html">2.9. Neural network models (unsupervised)</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="model_selection.html">3. Model selection and evaluation</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/cross_validation.html">3.1. Cross-validation: evaluating estimator performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/grid_search.html">3.2. Tuning the hyper-parameters of an estimator</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/classification_threshold.html">3.3. Tuning the decision threshold for class prediction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/model_evaluation.html">3.4. Metrics and scoring: quantifying the quality of predictions</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="metadata_routing.html">4. Metadata Routing</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/permutation_importance.html">5.2. Permutation feature importance</a></li>
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</details></li>
<li class="toctree-l1"><a class="reference internal" href="visualizations.html">6. Visualizations</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/compose.html">7.1. Pipelines and composite estimators</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_extraction.html">7.2. Feature extraction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing.html">7.3. Preprocessing data</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/impute.html">7.4. Imputation of missing values</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/random_projection.html">7.6. Random Projection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_approximation.html">7.7. Kernel Approximation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="datasets.html">8. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="datasets/toy_dataset.html">8.1. Toy datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/real_world.html">8.2. Real world datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/sample_generators.html">8.3. Generated datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="datasets/loading_other_datasets.html">8.4. Loading other datasets</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
<li class="toctree-l2"><a class="reference internal" href="computing/computational_performance.html">9.2. Computational Performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="model_persistence.html">10. Model persistence</a></li>
<li class="toctree-l1"><a class="reference internal" href="common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/array_api.html">12.1. Array API support (experimental)</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="machine_learning_map.html">13. Choosing the right estimator</a></li>
<li class="toctree-l1"><a class="reference internal" href="presentations.html">14. External Resources, Videos and Talks</a></li>
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<span id="id1"></span><h1>User Guide<a class="headerlink" href="#user-guide" title="Link to this heading">#</a></h1>
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<ul>
<li class="toctree-l1"><a class="reference internal" href="supervised_learning.html">1. Supervised learning</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html">1.1. Linear Models</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#ordinary-least-squares">1.1.1. Ordinary Least Squares</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#ridge-regression-and-classification">1.1.2. Ridge regression and classification</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#lasso">1.1.3. Lasso</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#multi-task-lasso">1.1.4. Multi-task Lasso</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#elastic-net">1.1.5. Elastic-Net</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#multi-task-elastic-net">1.1.6. Multi-task Elastic-Net</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#least-angle-regression">1.1.7. Least Angle Regression</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#lars-lasso">1.1.8. LARS Lasso</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#orthogonal-matching-pursuit-omp">1.1.9. Orthogonal Matching Pursuit (OMP)</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#bayesian-regression">1.1.10. Bayesian Regression</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#logistic-regression">1.1.11. Logistic regression</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#generalized-linear-models">1.1.12. Generalized Linear Models</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#stochastic-gradient-descent-sgd">1.1.13. Stochastic Gradient Descent - SGD</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#perceptron">1.1.14. Perceptron</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#passive-aggressive-algorithms">1.1.15. Passive Aggressive Algorithms</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#robustness-regression-outliers-and-modeling-errors">1.1.16. Robustness regression: outliers and modeling errors</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#quantile-regression">1.1.17. Quantile Regression</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/linear_model.html#polynomial-regression-extending-linear-models-with-basis-functions">1.1.18. Polynomial regression: extending linear models with basis functions</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/lda_qda.html">1.2. Linear and Quadratic Discriminant Analysis</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/lda_qda.html#dimensionality-reduction-using-linear-discriminant-analysis">1.2.1. Dimensionality reduction using Linear Discriminant Analysis</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/lda_qda.html#mathematical-formulation-of-the-lda-and-qda-classifiers">1.2.2. Mathematical formulation of the LDA and QDA classifiers</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/lda_qda.html#mathematical-formulation-of-lda-dimensionality-reduction">1.2.3. Mathematical formulation of LDA dimensionality reduction</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/lda_qda.html#shrinkage-and-covariance-estimator">1.2.4. Shrinkage and Covariance Estimator</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/lda_qda.html#estimation-algorithms">1.2.5. Estimation algorithms</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/kernel_ridge.html">1.3. Kernel ridge regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/svm.html">1.4. Support Vector Machines</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/svm.html#classification">1.4.1. Classification</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/svm.html#regression">1.4.2. Regression</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/svm.html#density-estimation-novelty-detection">1.4.3. Density estimation, novelty detection</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/svm.html#complexity">1.4.4. Complexity</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/svm.html#tips-on-practical-use">1.4.5. Tips on Practical Use</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/svm.html#kernel-functions">1.4.6. Kernel functions</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/svm.html#mathematical-formulation">1.4.7. Mathematical formulation</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/svm.html#implementation-details">1.4.8. Implementation details</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/sgd.html">1.5. Stochastic Gradient Descent</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/sgd.html#classification">1.5.1. Classification</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/sgd.html#regression">1.5.2. Regression</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/sgd.html#online-one-class-svm">1.5.3. Online One-Class SVM</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/sgd.html#stochastic-gradient-descent-for-sparse-data">1.5.4. Stochastic Gradient Descent for sparse data</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/sgd.html#complexity">1.5.5. Complexity</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/sgd.html#stopping-criterion">1.5.6. Stopping criterion</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/sgd.html#tips-on-practical-use">1.5.7. Tips on Practical Use</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/sgd.html#mathematical-formulation">1.5.8. Mathematical formulation</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/sgd.html#implementation-details">1.5.9. Implementation details</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/neighbors.html">1.6. Nearest Neighbors</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/neighbors.html#unsupervised-nearest-neighbors">1.6.1. Unsupervised Nearest Neighbors</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/neighbors.html#nearest-neighbors-classification">1.6.2. Nearest Neighbors Classification</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/neighbors.html#nearest-neighbors-regression">1.6.3. Nearest Neighbors Regression</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/neighbors.html#nearest-neighbor-algorithms">1.6.4. Nearest Neighbor Algorithms</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/neighbors.html#nearest-centroid-classifier">1.6.5. Nearest Centroid Classifier</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/neighbors.html#nearest-neighbors-transformer">1.6.6. Nearest Neighbors Transformer</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/neighbors.html#neighborhood-components-analysis">1.6.7. Neighborhood Components Analysis</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/gaussian_process.html">1.7. Gaussian Processes</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/gaussian_process.html#gaussian-process-regression-gpr">1.7.1. Gaussian Process Regression (GPR)</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/gaussian_process.html#gaussian-process-classification-gpc">1.7.2. Gaussian Process Classification (GPC)</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/gaussian_process.html#gpc-examples">1.7.3. GPC examples</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/gaussian_process.html#kernels-for-gaussian-processes">1.7.4. Kernels for Gaussian Processes</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/cross_decomposition.html">1.8. Cross decomposition</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/cross_decomposition.html#plscanonical">1.8.1. PLSCanonical</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/cross_decomposition.html#plssvd">1.8.2. PLSSVD</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/cross_decomposition.html#plsregression">1.8.3. PLSRegression</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/cross_decomposition.html#canonical-correlation-analysis">1.8.4. Canonical Correlation Analysis</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/naive_bayes.html">1.9. Naive Bayes</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/naive_bayes.html#gaussian-naive-bayes">1.9.1. Gaussian Naive Bayes</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/naive_bayes.html#multinomial-naive-bayes">1.9.2. Multinomial Naive Bayes</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/naive_bayes.html#complement-naive-bayes">1.9.3. Complement Naive Bayes</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/naive_bayes.html#bernoulli-naive-bayes">1.9.4. Bernoulli Naive Bayes</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/naive_bayes.html#categorical-naive-bayes">1.9.5. Categorical Naive Bayes</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/naive_bayes.html#out-of-core-naive-bayes-model-fitting">1.9.6. Out-of-core naive Bayes model fitting</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/tree.html">1.10. Decision Trees</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/tree.html#classification">1.10.1. Classification</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/tree.html#regression">1.10.2. Regression</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/tree.html#multi-output-problems">1.10.3. Multi-output problems</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/tree.html#complexity">1.10.4. Complexity</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/tree.html#tips-on-practical-use">1.10.5. Tips on practical use</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/tree.html#tree-algorithms-id3-c4-5-c5-0-and-cart">1.10.6. Tree algorithms: ID3, C4.5, C5.0 and CART</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/tree.html#mathematical-formulation">1.10.7. Mathematical formulation</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/tree.html#missing-values-support">1.10.8. Missing Values Support</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/tree.html#minimal-cost-complexity-pruning">1.10.9. Minimal Cost-Complexity Pruning</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/ensemble.html">1.11. Ensembles: Gradient boosting, random forests, bagging, voting, stacking</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/ensemble.html#gradient-boosted-trees">1.11.1. Gradient-boosted trees</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/ensemble.html#random-forests-and-other-randomized-tree-ensembles">1.11.2. Random forests and other randomized tree ensembles</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/ensemble.html#bagging-meta-estimator">1.11.3. Bagging meta-estimator</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/ensemble.html#voting-classifier">1.11.4. Voting Classifier</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/ensemble.html#voting-regressor">1.11.5. Voting Regressor</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/ensemble.html#stacked-generalization">1.11.6. Stacked generalization</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/ensemble.html#adaboost">1.11.7. AdaBoost</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/multiclass.html">1.12. Multiclass and multioutput algorithms</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/multiclass.html#multiclass-classification">1.12.1. Multiclass classification</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/multiclass.html#multilabel-classification">1.12.2. Multilabel classification</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/multiclass.html#multiclass-multioutput-classification">1.12.3. Multiclass-multioutput classification</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/multiclass.html#multioutput-regression">1.12.4. Multioutput regression</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_selection.html">1.13. Feature selection</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/feature_selection.html#removing-features-with-low-variance">1.13.1. Removing features with low variance</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/feature_selection.html#univariate-feature-selection">1.13.2. Univariate feature selection</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/feature_selection.html#recursive-feature-elimination">1.13.3. Recursive feature elimination</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/feature_selection.html#feature-selection-using-selectfrommodel">1.13.4. Feature selection using SelectFromModel</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/feature_selection.html#sequential-feature-selection">1.13.5. Sequential Feature Selection</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/feature_selection.html#feature-selection-as-part-of-a-pipeline">1.13.6. Feature selection as part of a pipeline</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/semi_supervised.html">1.14. Semi-supervised learning</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/semi_supervised.html#self-training">1.14.1. Self Training</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/semi_supervised.html#label-propagation">1.14.2. Label Propagation</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/isotonic.html">1.15. Isotonic regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/calibration.html">1.16. Probability calibration</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/calibration.html#calibration-curves">1.16.1. Calibration curves</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/calibration.html#calibrating-a-classifier">1.16.2. Calibrating a classifier</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/calibration.html#usage">1.16.3. Usage</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_supervised.html">1.17. Neural network models (supervised)</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/neural_networks_supervised.html#multi-layer-perceptron">1.17.1. Multi-layer Perceptron</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/neural_networks_supervised.html#classification">1.17.2. Classification</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/neural_networks_supervised.html#regression">1.17.3. Regression</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/neural_networks_supervised.html#regularization">1.17.4. Regularization</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/neural_networks_supervised.html#algorithms">1.17.5. Algorithms</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/neural_networks_supervised.html#complexity">1.17.6. Complexity</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/neural_networks_supervised.html#tips-on-practical-use">1.17.7. Tips on Practical Use</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/neural_networks_supervised.html#more-control-with-warm-start">1.17.8. More control with warm_start</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="unsupervised_learning.html">2. Unsupervised learning</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/mixture.html">2.1. Gaussian mixture models</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/mixture.html#gaussian-mixture">2.1.1. Gaussian Mixture</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/mixture.html#variational-bayesian-gaussian-mixture">2.1.2. Variational Bayesian Gaussian Mixture</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/manifold.html">2.2. Manifold learning</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/manifold.html#introduction">2.2.1. Introduction</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/manifold.html#isomap">2.2.2. Isomap</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/manifold.html#locally-linear-embedding">2.2.3. Locally Linear Embedding</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/manifold.html#modified-locally-linear-embedding">2.2.4. Modified Locally Linear Embedding</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/manifold.html#hessian-eigenmapping">2.2.5. Hessian Eigenmapping</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/manifold.html#spectral-embedding">2.2.6. Spectral Embedding</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/manifold.html#local-tangent-space-alignment">2.2.7. Local Tangent Space Alignment</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/manifold.html#multi-dimensional-scaling-mds">2.2.8. Multi-dimensional Scaling (MDS)</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/manifold.html#t-distributed-stochastic-neighbor-embedding-t-sne">2.2.9. t-distributed Stochastic Neighbor Embedding (t-SNE)</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/manifold.html#tips-on-practical-use">2.2.10. Tips on practical use</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/clustering.html">2.3. Clustering</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/clustering.html#overview-of-clustering-methods">2.3.1. Overview of clustering methods</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/clustering.html#k-means">2.3.2. K-means</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/clustering.html#affinity-propagation">2.3.3. Affinity Propagation</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/clustering.html#mean-shift">2.3.4. Mean Shift</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/clustering.html#spectral-clustering">2.3.5. Spectral clustering</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/clustering.html#hierarchical-clustering">2.3.6. Hierarchical clustering</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/clustering.html#dbscan">2.3.7. DBSCAN</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/clustering.html#hdbscan">2.3.8. HDBSCAN</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/clustering.html#optics">2.3.9. OPTICS</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/clustering.html#birch">2.3.10. BIRCH</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/clustering.html#clustering-performance-evaluation">2.3.11. Clustering performance evaluation</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/biclustering.html">2.4. Biclustering</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/biclustering.html#spectral-co-clustering">2.4.1. Spectral Co-Clustering</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/biclustering.html#spectral-biclustering">2.4.2. Spectral Biclustering</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/biclustering.html#biclustering-evaluation">2.4.3. Biclustering evaluation</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/decomposition.html">2.5. Decomposing signals in components (matrix factorization problems)</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/decomposition.html#principal-component-analysis-pca">2.5.1. Principal component analysis (PCA)</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/decomposition.html#kernel-principal-component-analysis-kpca">2.5.2. Kernel Principal Component Analysis (kPCA)</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/decomposition.html#truncated-singular-value-decomposition-and-latent-semantic-analysis">2.5.3. Truncated singular value decomposition and latent semantic analysis</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/decomposition.html#dictionary-learning">2.5.4. Dictionary Learning</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/decomposition.html#factor-analysis">2.5.5. Factor Analysis</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/decomposition.html#independent-component-analysis-ica">2.5.6. Independent component analysis (ICA)</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/decomposition.html#non-negative-matrix-factorization-nmf-or-nnmf">2.5.7. Non-negative matrix factorization (NMF or NNMF)</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/decomposition.html#latent-dirichlet-allocation-lda">2.5.8. Latent Dirichlet Allocation (LDA)</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/covariance.html">2.6. Covariance estimation</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/covariance.html#empirical-covariance">2.6.1. Empirical covariance</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/covariance.html#shrunk-covariance">2.6.2. Shrunk Covariance</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/covariance.html#sparse-inverse-covariance">2.6.3. Sparse inverse covariance</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/covariance.html#robust-covariance-estimation">2.6.4. Robust Covariance Estimation</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/outlier_detection.html">2.7. Novelty and Outlier Detection</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/outlier_detection.html#overview-of-outlier-detection-methods">2.7.1. Overview of outlier detection methods</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/outlier_detection.html#novelty-detection">2.7.2. Novelty Detection</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/outlier_detection.html#id1">2.7.3. Outlier Detection</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/outlier_detection.html#novelty-detection-with-local-outlier-factor">2.7.4. Novelty detection with Local Outlier Factor</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/density.html">2.8. Density Estimation</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/density.html#density-estimation-histograms">2.8.1. Density Estimation: Histograms</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/density.html#kernel-density-estimation">2.8.2. Kernel Density Estimation</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_unsupervised.html">2.9. Neural network models (unsupervised)</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/neural_networks_unsupervised.html#restricted-boltzmann-machines">2.9.1. Restricted Boltzmann machines</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="model_selection.html">3. Model selection and evaluation</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/cross_validation.html">3.1. Cross-validation: evaluating estimator performance</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/cross_validation.html#computing-cross-validated-metrics">3.1.1. Computing cross-validated metrics</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/cross_validation.html#cross-validation-iterators">3.1.2. Cross validation iterators</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/cross_validation.html#a-note-on-shuffling">3.1.3. A note on shuffling</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/cross_validation.html#cross-validation-and-model-selection">3.1.4. Cross validation and model selection</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/cross_validation.html#permutation-test-score">3.1.5. Permutation test score</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/grid_search.html">3.2. Tuning the hyper-parameters of an estimator</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/grid_search.html#exhaustive-grid-search">3.2.1. Exhaustive Grid Search</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/grid_search.html#randomized-parameter-optimization">3.2.2. Randomized Parameter Optimization</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/grid_search.html#searching-for-optimal-parameters-with-successive-halving">3.2.3. Searching for optimal parameters with successive halving</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/grid_search.html#tips-for-parameter-search">3.2.4. Tips for parameter search</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/grid_search.html#alternatives-to-brute-force-parameter-search">3.2.5. Alternatives to brute force parameter search</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/classification_threshold.html">3.3. Tuning the decision threshold for class prediction</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/classification_threshold.html#post-tuning-the-decision-threshold">3.3.1. Post-tuning the decision threshold</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/model_evaluation.html">3.4. Metrics and scoring: quantifying the quality of predictions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/model_evaluation.html#which-scoring-function-should-i-use">3.4.1. Which scoring function should I use?</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/model_evaluation.html#scoring-api-overview">3.4.2. Scoring API overview</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/model_evaluation.html#the-scoring-parameter-defining-model-evaluation-rules">3.4.3. The <code class="docutils literal notranslate"><span class="pre">scoring</span></code> parameter: defining model evaluation rules</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/model_evaluation.html#classification-metrics">3.4.4. Classification metrics</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/model_evaluation.html#multilabel-ranking-metrics">3.4.5. Multilabel ranking metrics</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/model_evaluation.html#regression-metrics">3.4.6. Regression metrics</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/model_evaluation.html#clustering-metrics">3.4.7. Clustering metrics</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/model_evaluation.html#dummy-estimators">3.4.8. Dummy estimators</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/learning_curve.html#validation-curve">3.5.1. Validation curve</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/learning_curve.html#learning-curve">3.5.2. Learning curve</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="metadata_routing.html">4. Metadata Routing</a><ul>
<li class="toctree-l2"><a class="reference internal" href="metadata_routing.html#usage-examples">4.1. Usage Examples</a><ul>
<li class="toctree-l3"><a class="reference internal" href="metadata_routing.html#weighted-scoring-and-fitting">4.1.1. Weighted scoring and fitting</a></li>
<li class="toctree-l3"><a class="reference internal" href="metadata_routing.html#weighted-scoring-and-unweighted-fitting">4.1.2. Weighted scoring and unweighted fitting</a></li>
<li class="toctree-l3"><a class="reference internal" href="metadata_routing.html#unweighted-feature-selection">4.1.3. Unweighted feature selection</a></li>
<li class="toctree-l3"><a class="reference internal" href="metadata_routing.html#different-scoring-and-fitting-weights">4.1.4. Different scoring and fitting weights</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="metadata_routing.html#api-interface">4.2. API Interface</a></li>
<li class="toctree-l2"><a class="reference internal" href="metadata_routing.html#metadata-routing-support-status">4.3. Metadata Routing Support Status</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="inspection.html">5. Inspection</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/partial_dependence.html#partial-dependence-plots">5.1.1. Partial dependence plots</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/partial_dependence.html#individual-conditional-expectation-ice-plot">5.1.2. Individual conditional expectation (ICE) plot</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/partial_dependence.html#mathematical-definition">5.1.3. Mathematical Definition</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/partial_dependence.html#computation-methods">5.1.4. Computation methods</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/permutation_importance.html">5.2. Permutation feature importance</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/permutation_importance.html#outline-of-the-permutation-importance-algorithm">5.2.1. Outline of the permutation importance algorithm</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/permutation_importance.html#relation-to-impurity-based-importance-in-trees">5.2.2. Relation to impurity-based importance in trees</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/permutation_importance.html#misleading-values-on-strongly-correlated-features">5.2.3. Misleading values on strongly correlated features</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="visualizations.html">6. Visualizations</a><ul>
<li class="toctree-l2"><a class="reference internal" href="visualizations.html#available-plotting-utilities">6.1. Available Plotting Utilities</a><ul>
<li class="toctree-l3"><a class="reference internal" href="visualizations.html#display-objects">6.1.1. Display Objects</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="data_transforms.html">7. Dataset transformations</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/compose.html">7.1. Pipelines and composite estimators</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/compose.html#pipeline-chaining-estimators">7.1.1. Pipeline: chaining estimators</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/compose.html#transforming-target-in-regression">7.1.2. Transforming target in regression</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/compose.html#featureunion-composite-feature-spaces">7.1.3. FeatureUnion: composite feature spaces</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/compose.html#columntransformer-for-heterogeneous-data">7.1.4. ColumnTransformer for heterogeneous data</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/compose.html#visualizing-composite-estimators">7.1.5. Visualizing Composite Estimators</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="modules/feature_extraction.html">7.2. Feature extraction</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/feature_extraction.html#loading-features-from-dicts">7.2.1. Loading features from dicts</a></li>