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,And:[239,250,255,259,783,787,825,834],Are:[227,233,235,313,314],Axes:2,Being:[783,791],But:[4,196,235,242,250,257,282,290,313,781],CVS:7,C_s:[95,817],DIS:[200,227],DNS:0,DTs:802,Das:338,Eds:798,FOR:237,For:[0,2,3,4,7,8,12,18,20,21,27,29,31,33,41,42,48,52,59,63,77,89,90,99,107,118,123,126,135,136,139,146,147,149,152,163,166,172,173,174,178,181,183,184,185,189,190,191,204,205,208,210,216,220,221,224,227,230,231,233,234,235,237,238,239,240,242,243,244,245,247,248,249,250,251,252,255,256,258,259,260,261,267,272,274,276,277,278,279,280,282,283,284,285,286,287,288,289,290,293,299,301,303,312,313,314,318,319,320,321,322,327,328,329,330,335,344,350,363,371,381,385,388,389,393,399,401,402,403,405,406,407,408,409,410,411,412,413,415,416,419,420,426,428,429,430,431,437,439,449,450,451,452,453,454,457,462,470,473,475,476,477,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,514,515,516,517,518,519,520,521,523,525,528,530,531,533,534,535,536,538,540,555,556,566,567,580,589,593,596,598,599,601,602,613,614,615,617,618,619,620,624,631,632,633,634,636,637,639,640,641,642,643,644,646,649,650,651,652,653,654,655,656,658,660,661,662,663,664,666,667,669,675,679,680,682,683,684,685,686,687,688,692,693,694,695,696,697,698,705,706,707,708,709,710,711,717,718,719,720,721,728,733,747,770,775,776,778,779,782,783,784,787,789,790,791,792,793,794,796,797,799,800,801,802,805,808,809,812,820,821,822,825,827,832,834,835],GPs:[261,465],Going:[430,431],His:251,IDS:[227,228,338],IDs:[180,340,342],Its:[50,227,243,256,261,657,782,793,801,809,830],Jas:[825,834],MDS:[147,148,149,150,535,823,824,827],NLS:238,NOT:[227,322,390,620],NVS:7,Nearness:475,Not:[94,234,235,242,250,252,299,301,313,393,470,473,530,534,584,613,624,648,651,653,806],OAS:[114,253,302,305,306,310,613],OLS:[115,116,137,146,783],ONE:[450,451],One:[4,7,15,16,19,52,53,59,70,114,144,166,168,185,206,227,243,246,248,250,252,256,257,258,259,277,286,299,301,303,307,308,319,320,360,500,508,510,515,517,518,617,618,640,641,709,747,778,783,784,785,786,787,791,792,800,801,807,823,824,831,833],PDs:416,PLS:[56,227,254,312,313,314,832],PRs:[235,239],PoS:259,Res:165,SVs:801,Such:[8,123,255,256,258,404,405,498,776,791,796,802,820],That:[164,235,239,243,260,279,287,406,411,438,613,624,634,784,785,787,794,802],The:[1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,17,19,20,21,23,24,26,27,29,30,31,32,33,35,37,42,43,44,45,49,50,51,52,53,54,55,60,62,65,70,71,72,74,76,77,78,79,80,81,83,84,85,87,88,89,90,91,98,99,101,103,105,106,107,108,110,111,112,114,115,116,117,118,120,121,125,126,128,129,131,133,135,137,139,146,148,150,153,155,158,159,160,162,164,165,167,168,169,170,171,173,176,177,180,181,182,183,184,186,187,188,189,191,192,193,194,195,196,199,200,201,203,206,208,212,213,214,216,217,218,219,220,222,223,224,225,226,228,230,232,234,235,236,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,256,257,258,260,261,262,263,267,269,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,296,297,298,299,300,301,302,303,304,305,307,308,309,310,311,312,313,314,315,316,317,318,321,325,326,327,328,329,330,332,333,335,338,339,340,341,342,343,344,345,347,349,350,351,353,354,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,422,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,448,449,450,451,452,453,454,455,456,457,458,459,461,462,463,464,465,466,467,468,470,471,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,528,529,530,531,532,533,534,535,536,537,538,539,542,543,544,545,546,548,551,552,553,554,555,556,557,558,559,560,563,564,565,566,568,569,573,575,576,579,581,582,585,586,589,593,594,595,596,597,598,599,600,601,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,690,691,692,693,694,695,696,697,698,699,700,701,702,703,705,706,707,708,709,710,711,718,719,720,721,722,725,728,729,732,733,734,738,739,741,749,750,751,755,768,770,774,775,776,777,778,779,780,781,782,783,784,785,788,790,791,792,793,794,796,800,801,802,803,805,806,807,808,809,810,812,813,814,816,817,818,819,822,823,824,825,827,828,829,830,831,832,833,834,835],Their:[227,232,250,487,488,827],Then:[79,168,234,235,238,247,250,256,259,260,438,470,473,551,613,624,776,781,786,787,794,801,810,822],There:[31,158,226,227,231,232,234,235,242,243,247,249,250,256,258,260,282,290,315,383,402,403,408,409,434,783,784,785,787,791,796,800,801,802,805,808,821,827],These:[36,64,68,79,82,135,166,173,175,184,196,202,227,234,235,240,242,243,245,247,249,250,252,254,256,258,259,260,261,276,280,344,356,357,363,446,510,518,540,593,658,765,775,780,781,783,784,787,789,797,801,802,806,812,817,822,825,827,833,835],Ties:[444,445,477,552],Tps:722,Use:[9,28,41,49,60,104,123,129,152,166,188,218,219,235,243,258,272,279,281,282,284,285,287,288,290,298,299,300,301,307,308,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,385,389,390,399,406,407,408,409,410,411,412,413,452,470,471,472,473,474,485,486,492,493,495,497,500,505,508,510,511,512,513,518,523,529,533,559,594,595,609,610,612,613,624,632,633,634,669,670,677,700,718,719,720,721,728,729,784,802,807,810,824,825,827,828,829,830,831,832,833,835],Used:[227,240,243,275,278,284,285,316,325,384,388,389,410,489,490,494,495,500,501,502,503,504,505,512,513,514,516,525,528,530,532,534,536,560,605,616,628,636,669,670,688,768],Useful:[236,241,293,296,297,298,299,300,301,302,303,304,305,306,309,310,395,500,501,525,526,527,806,828],Uses:[414,436,480,621,740,801,802],Using:[33,52,55,68,91,97,114,132,186,208,224,236,246,250,251,252,255,256,259,356,357,372,389,413,481,530,533,534,638,670,674,685,693,775,784,790,796,800,802,803,807,810,822,824,827,832],Will:[235,383,487,488,489,494,498,499,502,504,506,507,512,513,515,517,547,679,683,688,715,804,830,831,833],Willing:[825,833,834],With:[15,23,29,48,83,175,192,194,227,233,243,250,251,252,260,272,273,278,500,584,672,712,779,783,784,787,789,791,792,802,810,812,813,821,825,830,834],X_s:158,YES:243,_21:[489,490,494,495,502,503,504,505,521,523],______________________________________________________________________:202,________________________________________________________________________________:[6,39,186,218],__________________________________________________________________________________:41,________________________________________________________ward_tre:39,_____________________________________________________f_regress:39,________________________________________________fit_transform_on:186,___________________________________fit_transform_on:186,__array__:830,__call__:[208,243,259,261,455,456,457,458,459,460,461,462,463,464,465,466,467,468,825,834],__doc__:[1,2,4,6,7,8,10,11,12,13,14,15,16,17,18,19,20,21,23,24,25,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,80,81,82,83,84,86,87,88,89,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,135,136,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,155,157,158,159,160,162,163,164,165,167,168,169,170,171,172,173,174,177,178,179,181,182,183,184,185,186,187,189,190,191,194,195,196,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,243,593,818],__file__:[3,5,218,219],__future__:[1,3,5,6,7,8,9,10,11,12,22,42,49,60,89,94,159,162,166,184,186,188,192,196,199,200,218,219,220,251,818,820],__getstate__:[825,834],__init__:[3,10,208,235,237,238,243,259,262,263,264,265,266,267,268,272,274,275,276,277,278,279,280,281,282,284,285,292,293,296,297,298,299,300,301,302,303,304,305,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,380,381,382,383,384,385,386,387,388,389,390,391,392,393,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,426,427,428,433,434,435,436,437,438,439,440,441,442,443,444,445,446,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,470,471,472,473,475,476,477,480,481,482,483,484,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,529,530,531,532,533,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,665,666,667,668,669,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,699,700,702,703,705,706,707,708,709,710,711,718,719,720,721,769,828,833],__main__:[10,89,159,197,218,219,242],__name__:[2,10,24,43,89,159,197,242],__repr__:235,__traceback__:[417,418,419,420,421,422,423,424,425],__version__:[235,237,239],_basehmm:827,_beta_diverg:61,_build:235,_c_step:[825,834],_cfnode:276,_chang:235,_check_param_grid:833,_check_stop_list:436,_check_target:[825,834],_class_cov:[825,835],_class_mean:[825,835],_compon:385,_count_nonzero_coeffici:2,_decision_funct:[53,177],_dotbla:238,_error:787,_estimator_typ:[235,243,832],_fit_stag:[408,409],_fit_transform_on:186,_fro:[388,489,490,494,502,503,504,521],_func:[515,517],_generate_center_coordin:8,_generate_vector:197,_global:278,_idx:243,_init_t:800,_initialize_nmf:238,_learntselectormixin:832,_lfw:818,_loss:[243,787],_neg:238,_nls_subproblem:238,_not_in_sphinx:[3,5],_novalu:278,_pairwis:243,_pl:314,_po:238,_posterior_mod:453,_preprocess_d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us","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","Compressive sensing: tomography reconstruction with L1 prior (Lasso)","Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation","Libsvm GUI","Wikipedia principal eigenvector","Biclustering documents with the Spectral Co-clustering algorithm","A demo of the Spectral Biclustering algorithm","A demo of the Spectral Co-Clustering algorithm","Probability calibration of classifiers","Probability Calibration curves","Probability Calibration for 3-class classification","Comparison of Calibration of Classifiers","Plot classification probability","Classifier comparison","Recognizing hand-written digits","Normal and Shrinkage Linear Discriminant Analysis for classification","Linear and Quadratic Discriminant Analysis with covariance ellipsoid","Adjustment for chance in clustering performance evaluation","Demo of affinity propagation clustering algorithm","Agglomerative clustering with and without structure","Agglomerative clustering with different metrics","Compare BIRCH and MiniBatchKMeans","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","Demonstration of k-means assumptions","A demo of K-Means clustering on the handwritten digits data","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","Spectral clustering for image segmentation","Hierarchical clustering: structured vs unstructured ward","Column Transformer with Heterogeneous Data Sources","Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood","Ledoit-Wolf vs OAS estimation","Robust covariance estimation and Mahalanobis distances relevance","Outlier detection with several methods.","Robust vs Empirical covariance estimate","Sparse inverse covariance estimation","Compare cross decomposition methods","The Digit Dataset","The Iris Dataset","Plot randomly generated classification dataset","Plot randomly generated multilabel dataset","Beta-divergence loss functions","Faces dataset decompositions","Blind source separation using FastICA","FastICA on 2D point clouds","Image denoising using dictionary learning","Incremental PCA","Kernel PCA","Principal components analysis (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","Discrete versus Real AdaBoost","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","Feature importances with forests of trees","Pixel importances with a parallel forest of trees","Plot the decision surfaces of ensembles of trees on the iris dataset","Early stopping of Gradient Boosting","Gradient Boosting Out-of-Bag estimates","Prediction Intervals for Gradient Boosting Regression","Gradient Boosting regression","Gradient Boosting regularization","IsolationForest example","Partial Dependence Plots","Hashing feature transformation using Totally Random Trees","Comparing random forests and the multi-output meta estimator","Plot the decision boundaries of a VotingClassifier","Plot class probabilities calculated by the VotingClassifier","Cross-validation on diabetes Dataset Exercise","Cross-validation on Digits Dataset Exercise","Digits Classification Exercise","SVM Exercise","Comparison of F-test and mutual information","Univariate Feature Selection","Pipeline Anova SVM","Test with permutations the significance of a classification score","Recursive feature elimination","Recursive feature elimination with cross-validation","Feature selection using SelectFromModel and LassoCV","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","Gaussian process regression (GPR) on Mauna Loa CO2 data.","Gaussian process regression (GPR) with noise-level estimation","Gaussian Processes regression: basic introductory example","Illustration of prior and posterior Gaussian process for different kernels","Examples","Automatic Relevance Determination Regression (ARD)","Bayesian Ridge Regression","HuberRegressor vs Ridge on dataset with strong outliers","Logistic Regression 3-class Classifier","Lasso and Elastic Net for Sparse Signals","Lasso and Elastic Net","Lasso on dense and sparse data","Lasso path using LARS","Lasso model selection: Cross-Validation / AIC / BIC","Logistic function","L1 Penalty and Sparsity in Logistic Regression","Plot multinomial and One-vs-Rest Logistic Regression","Path with L1- Logistic Regression","Joint feature selection with multi-task Lasso","Linear Regression Example","Sparsity Example: Fitting only features 1  and 2","Ordinary Least Squares and Ridge Regression Variance","Orthogonal Matching Pursuit","Polynomial interpolation","Robust linear model estimation using RANSAC","Plot 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","Plot multi-class SGD on the iris dataset","SGD: convex loss functions","SGD: Penalties","SGD: Maximum margin separating hyperplane","SGD: Weighted samples","Multiclass sparse logisitic regression on newgroups20","MNIST classfification using multinomial logistic + L1","Theil-Sen Regression","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 reduction with LLE","t-SNE: The effect of various perplexity values on the shape","Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture","Gaussian Mixture Model Ellipsoids","GMM covariances","Density Estimation for a Gaussian mixture","Gaussian Mixture Model Selection","Gaussian Mixture Model Sine Curve","Sample pipeline for text feature extraction and evaluation","Confusion matrix","Plotting Cross-Validated Predictions","Parameter estimation using grid search with cross-validation","Plotting Learning Curves","Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV","Nested versus non-nested cross-validation","Precision-Recall","Comparing randomized search and grid search for hyperparameter estimation","Receiver Operating Characteristic (ROC)","Receiver Operating Characteristic (ROC) with cross validation","Train error vs Test error","Underfitting vs. Overfitting","Plotting Validation Curves","Classifier Chain","Nearest Neighbors Classification","Kernel Density Estimation","Simple 1D Kernel Density Estimation","Anomaly detection with Local Outlier Factor (LOF)","Nearest Centroid Classification","Nearest Neighbors regression","Kernel Density Estimate of Species Distributions","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","Comparing anomaly detection algorithms for outlier detection on toy datasets","Selecting dimensionality reduction with Pipeline and GridSearchCV","Pipelining: chaining a PCA and a logistic regression","Concatenating multiple feature extraction methods","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","Imputing missing values before building an estimator","Multilabel classification","Face completion with a multi-output estimators","Compare the effect of different scalers on data with outliers","Using FunctionTransformer to select columns","Using PowerTransformer to apply the Box-Cox transformation","Importance of Feature Scaling","Effect of transforming the targets in regression model","Label Propagation digits: Demonstrating performance","Label Propagation digits active learning","Label Propagation learning a complex structure","Decision boundary of label propagation versus SVM on the Iris dataset","SVM with custom kernel","Plot different SVM classifiers in the iris dataset","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","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: Weighted samples","Classification of text documents using sparse features","Clustering text documents using k-means","FeatureHasher and DictVectorizer Comparison","Plot the decision surface of a decision tree on the iris dataset","Decision Tree Regression","Multi-output Decision Tree Regression","Understanding the decision tree structure","4. Dataset transformations","5.6.5. Forest covertypes","5. Dataset loading utilities","5.6.7. Kddcup 99 dataset","5.6.4. The Labeled Faces in the Wild face recognition dataset","5.6.3. Downloading datasets from the mldata.org repository","5.6.1. The Olivetti faces dataset","5.6.6. RCV1 dataset","5.6.2. The 20 newsgroups text dataset","Advanced installation instructions","Contributing","Developer\u2019s Guide","Maintainer / core-developer information","How to optimize for speed","Developers\u2019 Tips and Tricks","Utilities for Developers","Documentation of scikit-learn 0.20.dev0","Frequently Asked Questions","Glossary of Common Terms and API Elements","&lt;no title&gt;","Installing scikit-learn","3. Model selection and evaluation","2.4. Biclustering","1.16. Probability calibration","API Reference","2.3. Clustering","4.1. Pipelines and composite estimators","7. Computational Performance","2.6. Covariance estimation","1.8. Cross decomposition","3.1. Cross-validation: evaluating estimator performance","2.5. Decomposing signals in components (matrix factorization problems)","2.8. Density Estimation","1.11. Ensemble methods","4.2. Feature extraction","1.13. Feature selection","1.7. 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class=\"docutils literal\"><span class=\"pre\">sklearn.covariance</span></code>.GraphicalLassoCV","<code class=\"docutils literal\"><span class=\"pre\">sklearn.covariance</span></code>.LedoitWolf","<code class=\"docutils literal\"><span class=\"pre\">sklearn.covariance</span></code>.MinCovDet","<code class=\"docutils literal\"><span class=\"pre\">sklearn.covariance</span></code>.OAS","<code class=\"docutils literal\"><span class=\"pre\">sklearn.covariance</span></code>.ShrunkCovariance","<code class=\"docutils literal\"><span class=\"pre\">sklearn.covariance</span></code>.empirical_covariance","<code class=\"docutils literal\"><span class=\"pre\">sklearn.covariance</span></code>.graph_lasso","<code class=\"docutils literal\"><span class=\"pre\">sklearn.covariance</span></code>.graphical_lasso","<code class=\"docutils literal\"><span class=\"pre\">sklearn.covariance</span></code>.ledoit_wolf","<code class=\"docutils literal\"><span 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class=\"pre\">sklearn.metrics</span></code>.median_absolute_error","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics</span></code>.mutual_info_score","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics</span></code>.normalized_mutual_info_score","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.additive_chi2_kernel","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.chi2_kernel","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.cosine_distances","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.cosine_similarity","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.distance_metrics","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.euclidean_distances","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.kernel_metrics","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.laplacian_kernel","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.linear_kernel","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.manhattan_distances","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.paired_cosine_distances","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.paired_distances","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.paired_euclidean_distances","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.paired_manhattan_distances","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.pairwise_kernels","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.polynomial_kernel","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.rbf_kernel","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics.pairwise</span></code>.sigmoid_kernel","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics</span></code>.pairwise_distances","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics</span></code>.pairwise_distances_argmin","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics</span></code>.pairwise_distances_argmin_min","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics</span></code>.pairwise_distances_chunked","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics</span></code>.precision_recall_curve","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics</span></code>.precision_recall_fscore_support","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics</span></code>.precision_score","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics</span></code>.r2_score","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics</span></code>.recall_score","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics</span></code>.roc_auc_score","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics</span></code>.roc_curve","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics</span></code>.silhouette_samples","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics</span></code>.silhouette_score","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics</span></code>.v_measure_score","<code class=\"docutils literal\"><span class=\"pre\">sklearn.metrics</span></code>.zero_one_loss","<code class=\"docutils literal\"><span class=\"pre\">sklearn.mixture</span></code>.BayesianGaussianMixture","<code class=\"docutils literal\"><span class=\"pre\">sklearn.mixture</span></code>.DPGMM","<code class=\"docutils literal\"><span class=\"pre\">sklearn.mixture</span></code>.GMM","<code class=\"docutils literal\"><span class=\"pre\">sklearn.mixture</span></code>.GaussianMixture","<code class=\"docutils literal\"><span class=\"pre\">sklearn.mixture</span></code>.VBGMM","<code class=\"docutils literal\"><span class=\"pre\">sklearn.model_selection</span></code>.GridSearchCV","<code class=\"docutils literal\"><span class=\"pre\">sklearn.model_selection</span></code>.GroupKFold","<code class=\"docutils literal\"><span class=\"pre\">sklearn.model_selection</span></code>.GroupShuffleSplit","<code class=\"docutils literal\"><span class=\"pre\">sklearn.model_selection</span></code>.KFold","<code class=\"docutils literal\"><span class=\"pre\">sklearn.model_selection</span></code>.LeaveOneGroupOut","<code class=\"docutils literal\"><span class=\"pre\">sklearn.model_selection</span></code>.LeaveOneOut","<code class=\"docutils literal\"><span class=\"pre\">sklearn.model_selection</span></code>.LeavePGroupsOut","<code class=\"docutils literal\"><span class=\"pre\">sklearn.model_selection</span></code>.LeavePOut","<code class=\"docutils literal\"><span class=\"pre\">sklearn.model_selection</span></code>.ParameterGrid","<code class=\"docutils literal\"><span class=\"pre\">sklearn.model_selection</span></code>.ParameterSampler","<code class=\"docutils literal\"><span class=\"pre\">sklearn.model_selection</span></code>.PredefinedSplit","<code class=\"docutils literal\"><span class=\"pre\">sklearn.model_selection</span></code>.RandomizedSearchCV","<code class=\"docutils literal\"><span class=\"pre\">sklearn.model_selection</span></code>.RepeatedKFold","<code class=\"docutils literal\"><span class=\"pre\">sklearn.model_selection</span></code>.RepeatedStratifiedKFold","<code class=\"docutils literal\"><span class=\"pre\">sklearn.model_selection</span></code>.ShuffleSplit","<code class=\"docutils literal\"><span class=\"pre\">sklearn.model_selection</span></code>.StratifiedKFold","<code class=\"docutils literal\"><span class=\"pre\">sklearn.model_selection</span></code>.StratifiedShuffleSplit","<code class=\"docutils literal\"><span class=\"pre\">sklearn.model_selection</span></code>.TimeSeriesSplit","<code class=\"docutils literal\"><span class=\"pre\">sklearn.model_selection</span></code>.check_cv","<code class=\"docutils literal\"><span class=\"pre\">sklearn.model_selection</span></code>.cross_val_predict","<code class=\"docutils literal\"><span class=\"pre\">sklearn.model_selection</span></code>.cross_val_score","<code class=\"docutils literal\"><span class=\"pre\">sklearn.model_selection</span></code>.cross_validate","<code class=\"docutils literal\"><span class=\"pre\">sklearn.model_selection</span></code>.fit_grid_point","<code class=\"docutils literal\"><span class=\"pre\">sklearn.model_selection</span></code>.learning_curve","<code class=\"docutils literal\"><span class=\"pre\">sklearn.model_selection</span></code>.permutation_test_score","<code class=\"docutils literal\"><span class=\"pre\">sklearn.model_selection</span></code>.train_test_split","<code class=\"docutils literal\"><span class=\"pre\">sklearn.model_selection</span></code>.validation_curve","<code class=\"docutils literal\"><span class=\"pre\">sklearn.multiclass</span></code>.OneVsOneClassifier","<code class=\"docutils literal\"><span class=\"pre\">sklearn.multiclass</span></code>.OneVsRestClassifier","<code class=\"docutils literal\"><span class=\"pre\">sklearn.multiclass</span></code>.OutputCodeClassifier","<code class=\"docutils literal\"><span class=\"pre\">sklearn.multioutput</span></code>.ClassifierChain","<code class=\"docutils literal\"><span class=\"pre\">sklearn.multioutput</span></code>.MultiOutputClassifier","<code class=\"docutils literal\"><span class=\"pre\">sklearn.multioutput</span></code>.MultiOutputRegressor","<code class=\"docutils literal\"><span class=\"pre\">sklearn.multioutput</span></code>.RegressorChain","<code class=\"docutils literal\"><span class=\"pre\">sklearn.naive_bayes</span></code>.BernoulliNB","<code class=\"docutils literal\"><span class=\"pre\">sklearn.naive_bayes</span></code>.ComplementNB","<code class=\"docutils literal\"><span class=\"pre\">sklearn.naive_bayes</span></code>.GaussianNB","<code class=\"docutils literal\"><span class=\"pre\">sklearn.naive_bayes</span></code>.MultinomialNB","<code class=\"docutils literal\"><span class=\"pre\">sklearn.neighbors</span></code>.BallTree","<code class=\"docutils literal\"><span class=\"pre\">sklearn.neighbors</span></code>.DistanceMetric","<code class=\"docutils literal\"><span class=\"pre\">sklearn.neighbors</span></code>.KDTree","<code class=\"docutils literal\"><span class=\"pre\">sklearn.neighbors</span></code>.KNeighborsClassifier","<code class=\"docutils literal\"><span class=\"pre\">sklearn.neighbors</span></code>.KNeighborsRegressor","<code class=\"docutils literal\"><span class=\"pre\">sklearn.neighbors</span></code>.KernelDensity","<code class=\"docutils literal\"><span class=\"pre\">sklearn.neighbors</span></code>.LSHForest","<code class=\"docutils literal\"><span class=\"pre\">sklearn.neighbors</span></code>.LocalOutlierFactor","<code class=\"docutils literal\"><span class=\"pre\">sklearn.neighbors</span></code>.NearestCentroid","<code class=\"docutils literal\"><span class=\"pre\">sklearn.neighbors</span></code>.NearestNeighbors","<code class=\"docutils literal\"><span class=\"pre\">sklearn.neighbors</span></code>.RadiusNeighborsClassifier","<code class=\"docutils literal\"><span class=\"pre\">sklearn.neighbors</span></code>.RadiusNeighborsRegressor","<code class=\"docutils literal\"><span class=\"pre\">sklearn.neighbors</span></code>.kneighbors_graph","<code class=\"docutils literal\"><span class=\"pre\">sklearn.neighbors</span></code>.radius_neighbors_graph","<code class=\"docutils literal\"><span class=\"pre\">sklearn.neural_network</span></code>.BernoulliRBM","<code class=\"docutils literal\"><span class=\"pre\">sklearn.neural_network</span></code>.MLPClassifier","<code class=\"docutils literal\"><span class=\"pre\">sklearn.neural_network</span></code>.MLPRegressor","<code class=\"docutils literal\"><span class=\"pre\">sklearn.pipeline</span></code>.FeatureUnion","<code class=\"docutils literal\"><span class=\"pre\">sklearn.pipeline</span></code>.Pipeline","<code class=\"docutils literal\"><span class=\"pre\">sklearn.pipeline</span></code>.make_pipeline","<code class=\"docutils literal\"><span class=\"pre\">sklearn.pipeline</span></code>.make_union","<code class=\"docutils literal\"><span class=\"pre\">sklearn.preprocessing</span></code>.Binarizer","<code class=\"docutils literal\"><span class=\"pre\">sklearn.preprocessing</span></code>.CategoricalEncoder","<code class=\"docutils literal\"><span class=\"pre\">sklearn.preprocessing</span></code>.FunctionTransformer","<code class=\"docutils literal\"><span class=\"pre\">sklearn.preprocessing</span></code>.Imputer","<code class=\"docutils literal\"><span class=\"pre\">sklearn.preprocessing</span></code>.KernelCenterer","<code class=\"docutils literal\"><span class=\"pre\">sklearn.preprocessing</span></code>.LabelBinarizer","<code class=\"docutils literal\"><span class=\"pre\">sklearn.preprocessing</span></code>.LabelEncoder","<code class=\"docutils literal\"><span class=\"pre\">sklearn.preprocessing</span></code>.MaxAbsScaler","<code class=\"docutils literal\"><span class=\"pre\">sklearn.preprocessing</span></code>.MinMaxScaler","<code class=\"docutils literal\"><span class=\"pre\">sklearn.preprocessing</span></code>.MultiLabelBinarizer","<code class=\"docutils literal\"><span class=\"pre\">sklearn.preprocessing</span></code>.Normalizer","<code class=\"docutils literal\"><span class=\"pre\">sklearn.preprocessing</span></code>.OneHotEncoder","<code class=\"docutils literal\"><span class=\"pre\">sklearn.preprocessing</span></code>.PolynomialFeatures","<code class=\"docutils literal\"><span class=\"pre\">sklearn.preprocessing</span></code>.PowerTransformer","<code class=\"docutils literal\"><span class=\"pre\">sklearn.preprocessing</span></code>.QuantileTransformer","<code class=\"docutils literal\"><span class=\"pre\">sklearn.preprocessing</span></code>.RobustScaler","<code class=\"docutils literal\"><span class=\"pre\">sklearn.preprocessing</span></code>.StandardScaler","<code class=\"docutils literal\"><span class=\"pre\">sklearn.preprocessing</span></code>.add_dummy_feature","<code class=\"docutils literal\"><span class=\"pre\">sklearn.preprocessing</span></code>.binarize","<code class=\"docutils literal\"><span class=\"pre\">sklearn.preprocessing</span></code>.label_binarize","<code class=\"docutils literal\"><span class=\"pre\">sklearn.preprocessing</span></code>.maxabs_scale","<code class=\"docutils literal\"><span class=\"pre\">sklearn.preprocessing</span></code>.minmax_scale","<code class=\"docutils literal\"><span class=\"pre\">sklearn.preprocessing</span></code>.normalize","<code class=\"docutils literal\"><span class=\"pre\">sklearn.preprocessing</span></code>.power_transform","<code class=\"docutils literal\"><span class=\"pre\">sklearn.preprocessing</span></code>.quantile_transform","<code class=\"docutils literal\"><span class=\"pre\">sklearn.preprocessing</span></code>.robust_scale","<code class=\"docutils literal\"><span class=\"pre\">sklearn.preprocessing</span></code>.scale","<code class=\"docutils literal\"><span class=\"pre\">sklearn.random_projection</span></code>.GaussianRandomProjection","<code class=\"docutils literal\"><span class=\"pre\">sklearn.random_projection</span></code>.SparseRandomProjection","<code class=\"docutils literal\"><span class=\"pre\">sklearn.random_projection</span></code>.johnson_lindenstrauss_min_dim","<code class=\"docutils literal\"><span class=\"pre\">sklearn.semi_supervised</span></code>.LabelPropagation","<code class=\"docutils literal\"><span class=\"pre\">sklearn.semi_supervised</span></code>.LabelSpreading","<code class=\"docutils literal\"><span class=\"pre\">sklearn</span></code>.set_config","<code class=\"docutils literal\"><span class=\"pre\">sklearn.svm</span></code>.LinearSVC","<code class=\"docutils literal\"><span class=\"pre\">sklearn.svm</span></code>.LinearSVR","<code class=\"docutils literal\"><span class=\"pre\">sklearn.svm</span></code>.NuSVC","<code class=\"docutils literal\"><span class=\"pre\">sklearn.svm</span></code>.NuSVR","<code class=\"docutils literal\"><span class=\"pre\">sklearn.svm</span></code>.OneClassSVM","<code class=\"docutils literal\"><span class=\"pre\">sklearn.svm</span></code>.SVC","<code class=\"docutils literal\"><span class=\"pre\">sklearn.svm</span></code>.SVR","<code class=\"docutils literal\"><span class=\"pre\">sklearn.svm</span></code>.l1_min_c","<code class=\"docutils literal\"><span class=\"pre\">sklearn.svm.libsvm</span></code>.cross_validation","<code class=\"docutils literal\"><span class=\"pre\">sklearn.svm.libsvm</span></code>.decision_function","<code class=\"docutils literal\"><span class=\"pre\">sklearn.svm.libsvm</span></code>.fit","<code class=\"docutils literal\"><span class=\"pre\">sklearn.svm.libsvm</span></code>.predict","<code class=\"docutils literal\"><span class=\"pre\">sklearn.svm.libsvm</span></code>.predict_proba","<code class=\"docutils literal\"><span class=\"pre\">sklearn.tree</span></code>.DecisionTreeClassifier","<code class=\"docutils literal\"><span class=\"pre\">sklearn.tree</span></code>.DecisionTreeRegressor","<code class=\"docutils literal\"><span class=\"pre\">sklearn.tree</span></code>.ExtraTreeClassifier","<code class=\"docutils literal\"><span class=\"pre\">sklearn.tree</span></code>.ExtraTreeRegressor","<code class=\"docutils literal\"><span class=\"pre\">sklearn.tree</span></code>.export_graphviz","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.arrayfuncs</span></code>.cholesky_delete","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.arrayfuncs</span></code>.min_pos","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils</span></code>.as_float_array","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils</span></code>.assert_all_finite","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.bench</span></code>.total_seconds","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils</span></code>.check_X_y","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils</span></code>.check_array","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils</span></code>.check_consistent_length","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils</span></code>.check_random_state","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.class_weight</span></code>.compute_class_weight","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.class_weight</span></code>.compute_sample_weight","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils</span></code>.deprecated","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.estimator_checks</span></code>.check_estimator","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.extmath</span></code>.density","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.extmath</span></code>.fast_logdet","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.extmath</span></code>.randomized_range_finder","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.extmath</span></code>.randomized_svd","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.extmath</span></code>.safe_sparse_dot","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.extmath</span></code>.weighted_mode","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils</span></code>.gen_even_slices","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.graph</span></code>.single_source_shortest_path_length","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.graph_shortest_path</span></code>.graph_shortest_path","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils</span></code>.indexable","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.multiclass</span></code>.is_multilabel","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.multiclass</span></code>.type_of_target","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.multiclass</span></code>.unique_labels","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils</span></code>.murmurhash3_32","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.random</span></code>.sample_without_replacement","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils</span></code>.resample","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils</span></code>.safe_indexing","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils</span></code>.safe_mask","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils</span></code>.safe_sqr","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils</span></code>.shuffle","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.sparsefuncs</span></code>.incr_mean_variance_axis","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.sparsefuncs</span></code>.inplace_column_scale","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.sparsefuncs</span></code>.inplace_csr_column_scale","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.sparsefuncs</span></code>.inplace_row_scale","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.sparsefuncs</span></code>.inplace_swap_column","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.sparsefuncs</span></code>.inplace_swap_row","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.sparsefuncs</span></code>.mean_variance_axis","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.sparsefuncs_fast</span></code>.inplace_csr_row_normalize_l1","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.sparsefuncs_fast</span></code>.inplace_csr_row_normalize_l2","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.testing</span></code>.all_estimators","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.testing</span></code>.assert_in","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.testing</span></code>.assert_not_in","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.testing</span></code>.assert_raise_message","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.testing</span></code>.mock_mldata_urlopen","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.validation</span></code>.check_is_fitted","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.validation</span></code>.check_memory","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.validation</span></code>.check_symmetric","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.validation</span></code>.column_or_1d","<code class=\"docutils literal\"><span class=\"pre\">sklearn.utils.validation</span></code>.has_fit_parameter","3.2. Tuning the hyper-parameters of an estimator","4.4. Imputation of missing values","1.15. Isotonic regression","4.7. Kernel Approximation","1.3. Kernel ridge regression","1.14. Semi-Supervised","1.2. Linear and Quadratic Discriminant Analysis","3.5. Validation curves: plotting scores to evaluate models","1.1. Generalized Linear Models","2.2. Manifold learning","4.8. Pairwise metrics, Affinities and Kernels","2.1. Gaussian mixture models","3.3. Model evaluation: quantifying the quality of predictions","3.4. Model persistence","1.12. Multiclass and multilabel algorithms","1.9. Naive Bayes","1.6. Nearest Neighbors","1.17. Neural network models (supervised)","2.9. Neural network models (unsupervised)","2.7. Novelty and Outlier Detection","&lt;no title&gt;","4.3. Preprocessing data","4.9. Transforming the prediction target (<code class=\"docutils literal\"><span class=\"pre\">y</span></code>)","4.6. Random Projection","6. Strategies to scale computationally: bigger data","1.5. Stochastic Gradient Descent","1.4. Support Vector Machines","1.10. Decision Trees","4.5. Unsupervised dimensionality reduction","scikit-learn","External Resources, Videos and Talks","Related Projects","1. Supervised learning","Support","Who is using scikit-learn?","Machine Learning Cheat Sheet (for scikit-learn)","&lt;no title&gt;","An introduction to machine learning with scikit-learn","scikit-learn Tutorials","Choosing the right estimator","Finding help","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","2. Unsupervised learning","User guide: contents","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 0.19.1","Version 0.20 (under 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