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387,398,406,409,411,519,731,998,1002,1005,1035,1040,1044],"Mostly":1021,"My":370,"NOT":[238,288,368,374,561,813,847,848],"No":[61,147,164,193,211,212,238,256,263,265,325,342,368,380,390,398,404,471,557,558,589,590,609,635,645,664,676,678,685,686,800,944,998,1050],"Norred":[1053,1054],"Not":[51,87,103,183,243,261,263,358,384,398,404,422,423,426,437,438,439,440,441,442,444,445,446,447,448,449,450,466,467,468,469,470,471,472,473,529,531,532,533,534,535,536,537,538,539,540,541,542,561,564,567,568,580,581,582,587,588,590,627,628,629,630,677,688,689,690,691,692,694,797,800,801,803,806,807,817,844,847,848,851,852,853,855,859,871,879,882,883,885,888,899,900,911,1002,1008,1021,1042],"OF":368,"ON":316,"OR":[302,404,415,441,1044],"Of":[48,62,64,177,276,358,372,404,412,992],"Off":[114,366],"On":[43,51,52,55,63,102,103,114,115,124,133,136,138,140,142,145,149,150,153,154,155,157,159,170,171,182,183,184,191,195,197,207,209,232,233,234,235,237,238,241,242,243,244,245,247,249,252,256,257,258,259,260,261,262,263,264,265,267,269,274,276,300,302,304,306,307,308,312,313,315,316,318,321,322,326,335,342,352,353,358,370,371,372,373,375,384,386,400,402,404,408,409,411,413,414,434,643,656,692,738,764,872,888,991,995,998,1001,1002,1004,1008,1010,1015,1017],"Or":[346,353,408,409,880,1002,1008,1016],"Other":[43,72,102,142,179,182,301,309,365,366,367,370,372,380,384,386,409,461,494,560,587,588,590,668,672,674,830,868,880,881,888,1012,1018,1023,1034,1039,1040,1041,1047],"Others":[367,1002],"Our":[0,90,115,166,171,182,205,222,262,268,319,346,375,376,380,386,395,412,414,415,441,447,709,722,992,1002,1026,1040,1043],"Out":[1,42,49,132,137,147,153,179,182,209,212,238,260,314,360,372,386,411,412,497,514,557,588,668,673,675,676,744,805,808,810,811,812,813,833,846,998,1023,1024,1028,1033,1035,1037],"Over":[359,944,945,946,1026],"Same":[282,328,366,425,491,496,589,627,666,667,668,676,677,678,782,783,870,876,877,879,881,883,884,885,887,889,999,1046,1054],"Should":[242,313,370,455,479,480,481,482,514,677,693,697,835,836,864,865,904,909,910,911,933,976,1012],"So":[190,256,275,279,282,346,358,370,386,398,412,666,667,676,677,678,693,781,991,1002,1014],"Some":[78,102,138,154,158,179,183,189,193,209,222,232,233,243,245,256,261,276,300,312,315,321,322,353,354,358,359,366,371,375,376,379,380,386,390,400,401,402,404,408,410,412,546,658,659,709,991,992,1002,1003,1012,1017,1018,1021,1022,1025,1033,1039,1040,1042,1045,1052,1055],"Such":[53,142,149,166,171,181,189,193,197,206,256,265,346,386,387,398,408,409,411,553,554,656,992,1002,1005,1012,1018,1043,1048,1050],"THE":368,"THEIR":370,"That":[52,64,119,133,148,162,182,197,231,262,266,313,347,348,352,353,359,365,372,380,386,403,408,413,414,440,443,444,555,562,592,821,915,916,991,999,1000,1008,1018,1042,1043],"The":[1,15,25,27,30,31,33,37,43,44,45,46,47,48,49,50,51,52,53,54,55,57,58,59,61,62,63,64,66,67,68,69,70,72,73,74,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,101,102,103,104,105,106,108,109,110,111,112,114,117,119,120,121,122,123,124,125,126,127,128,129,131,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,157,158,159,160,161,162,164,166,167,168,169,170,171,172,173,174,175,177,178,179,183,184,185,187,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,230,231,232,233,234,236,237,238,239,240,241,242,243,244,245,247,248,249,250,251,252,253,255,257,258,259,261,262,263,264,265,266,267,268,270,271,272,273,274,275,277,281,282,284,285,286,287,288,289,290,291,292,293,294,295,297,298,299,300,302,303,304,305,306,307,308,309,311,312,313,314,315,316,317,318,319,320,321,322,324,325,326,327,328,329,331,332,333,334,335,336,337,338,339,340,341,342,343,344,346,347,348,350,351,352,353,354,358,359,363,364,365,367,368,369,370,371,372,373,374,376,377,378,379,381,384,385,386,387,389,390,392,395,398,399,400,401,402,403,404,405,406,407,409,410,411,413,414,415,416,417,418,419,420,424,426,427,429,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,464,465,466,467,468,469,470,471,472,473,475,476,477,478,479,480,481,482,483,484,485,486,487,488,490,491,492,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,687,688,689,690,691,692,693,694,695,696,6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us","API Reference","sklearn","sklearn.base","sklearn.calibration","sklearn.cluster","sklearn.compose","sklearn.covariance","sklearn.cross_decomposition","sklearn.datasets","sklearn.decomposition","sklearn.discriminant_analysis","sklearn.dummy","sklearn.ensemble","sklearn.exceptions","sklearn.experimental","sklearn.feature_extraction","sklearn.feature_selection","sklearn.frozen","sklearn.gaussian_process","sklearn.impute","sklearn.inspection","sklearn.isotonic","sklearn.kernel_approximation","sklearn.kernel_ridge","sklearn.linear_model","sklearn.manifold","sklearn.metrics","sklearn.mixture","sklearn.model_selection","sklearn.multiclass","sklearn.multioutput","sklearn.naive_bayes","sklearn.neighbors","sklearn.neural_network","sklearn.pipeline","sklearn.preprocessing","sklearn.random_projection","sklearn.semi_supervised","sklearn.svm","sklearn.tree","sklearn.utils","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","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","Calibration","Probability calibration of classifiers","Probability Calibration curves","Probability Calibration for 3-class classification","Comparison of Calibration of Classifiers","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","Clustering","Adjustment for chance in clustering performance evaluation","Demo of affinity propagation clustering algorithm","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","Segmenting the picture of greek coins in regions","A demo of structured Ward hierarchical clustering on an image of coins","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 with and without structure","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","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","Cross decomposition","Compare cross decomposition methods","Principal Component Regression vs Partial Least Squares Regression","Dataset examples","Plot randomly generated multilabel dataset","Decomposition","Faces dataset decompositions","Blind source separation using FastICA","FastICA on 2D point clouds","Image denoising using dictionary learning","Incremental PCA","Kernel PCA","Principal Component Analysis (PCA) on Iris Dataset","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","Developing Estimators","__sklearn_is_fitted__ 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","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","Features in Histogram Gradient Boosting Trees","IsolationForest example","Monotonic Constraints","Hashing feature transformation using Totally Random Trees","Comparing random forests and the multi-output meta estimator","Combine predictors using stacking","Visualizing the probabilistic predictions of a VotingClassifier","Plot individual and voting regression predictions","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","Frozen Estimators","Examples of Using FrozenEstimator","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","Missing Value Imputation","Imputing missing values with variants of IterativeImputer","Imputing missing values before building an estimator","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","Kernel Approximation","Scalable learning with polynomial kernel approximation","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","L1-based models for Sparse Signals","Lasso on dense and sparse data","Lasso model selection via information criteria","Lasso, Lasso-LARS, and Elastic Net paths","Lasso model selection: AIC-BIC / cross-validation","L1 Penalty and Sparsity in Logistic Regression","Decision Boundaries of Multinomial and One-vs-Rest Logistic Regression","Regularization path of L1- Logistic Regression","Joint feature selection with multi-task Lasso","Non-negative least squares","Ordinary Least Squares and Ridge Regression","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","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 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estimators","Evaluation of outlier detection estimators","Advanced Plotting With Partial Dependence","Displaying Pipelines","ROC Curve with Visualization API","Introducing the set_output API","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","Model Selection","Evaluate the performance of a classifier with Confusion Matrix","Post-tuning the decision threshold for cost-sensitive learning","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 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