Update README.md
Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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@ -147,80 +147,38 @@ see [here](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.d
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discriminant_analysis.LinearDiscriminantAnalysis([...])
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Linear Discriminant Analysis.
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discriminant_analysis.QuadraticDiscriminantAnalysis(*)
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Quadratic Discriminant Analysis.
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|discriminant_analysis.LinearDiscriminantAnalysis([...])|Linear Discriminant Analysis.|
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|discriminant_analysis.QuadraticDiscriminantAnalysis(*)|Quadratic Discriminant Analysis.|
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## [sklearn.dummy: Dummy estimators](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.dummy)
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dummy.DummyClassifier(*[, strategy, ...])
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DummyClassifier makes predictions that ignore the input features.
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dummy.DummyRegressor(*[, strategy, ...])
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Regressor that makes predictions using simple rules.
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|dummy.DummyClassifier(*[, strategy, ...])|DummyClassifier makes predictions that ignore the input features.|
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|dummy.DummyRegressor(*[, strategy, ...])|Regressor that makes predictions using simple rules.|
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## [sklearn.ensemble: Ensemble Methods](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.ensemble)
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ensemble.AdaBoostClassifier([estimator, ...])
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An AdaBoost classifier.
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ensemble.AdaBoostRegressor([estimator, ...])
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An AdaBoost regressor.
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ensemble.BaggingClassifier([estimator, ...])
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A Bagging classifier.
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ensemble.BaggingRegressor([estimator, ...])
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A Bagging regressor.
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ensemble.ExtraTreesClassifier([...])
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An extra-trees classifier.
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ensemble.ExtraTreesRegressor([n_estimators, ...])
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An extra-trees regressor.
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ensemble.GradientBoostingClassifier(*[, ...])
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Gradient Boosting for classification.
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ensemble.GradientBoostingRegressor(*[, ...])
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Gradient Boosting for regression.
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ensemble.IsolationForest(*[, n_estimators, ...])
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Isolation Forest Algorithm.
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ensemble.RandomForestClassifier([...])
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A random forest classifier.
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ensemble.RandomForestRegressor([...])
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A random forest regressor.
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ensemble.RandomTreesEmbedding([...])
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An ensemble of totally random trees.
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ensemble.StackingClassifier(estimators[, ...])
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Stack of estimators with a final classifier.
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ensemble.StackingRegressor(estimators[, ...])
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Stack of estimators with a final regressor.
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ensemble.VotingClassifier(estimators, *[, ...])
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Soft Voting/Majority Rule classifier for unfitted estimators.
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ensemble.VotingRegressor(estimators, *[, ...])
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Prediction voting regressor for unfitted estimators.
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ensemble.HistGradientBoostingRegressor([...])
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Histogram-based Gradient Boosting Regression Tree.
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ensemble.HistGradientBoostingClassifier([...])
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Histogram-based Gradient Boosting Classification Tree.
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|ensemble.AdaBoostClassifier([estimator, ...])|An AdaBoost classifier.|
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|ensemble.AdaBoostRegressor([estimator, ...])|An AdaBoost regressor.|
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|ensemble.BaggingClassifier([estimator, ...])|A Bagging classifier.|
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|ensemble.BaggingRegressor([estimator, ...])|A Bagging regressor.|
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|ensemble.ExtraTreesClassifier([...])|An extra-trees classifier.|
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|ensemble.ExtraTreesRegressor([n_estimators, ...])|An extra-trees regressor.|
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|ensemble.GradientBoostingClassifier(*[, ...])|Gradient Boosting for classification.|
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|ensemble.GradientBoostingRegressor(*[, ...])|Gradient Boosting for regression.|
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|ensemble.IsolationForest(*[, n_estimators, ...])|Isolation Forest Algorithm.|
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|ensemble.RandomForestClassifier([...])|A random forest classifier.|
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|ensemble.RandomForestRegressor([...])|A random forest regressor.|
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|ensemble.RandomTreesEmbedding([...])|An ensemble of totally random trees.|
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|ensemble.StackingClassifier(estimators[, ...])|Stack of estimators with a final classifier.|
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|ensemble.StackingRegressor(estimators[, ...])|Stack of estimators with a final regressor.|
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|ensemble.VotingClassifier(estimators, *[, ...])|Soft Voting/Majority Rule classifier for unfitted estimators.|
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|ensemble.VotingRegressor(estimators, *[, ...])|Prediction voting regressor for unfitted estimators.|
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|ensemble.HistGradientBoostingRegressor([...])|Histogram-based Gradient Boosting Regression Tree.|
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|ensemble.HistGradientBoostingClassifier([...])|Histogram-based Gradient Boosting Classification Tree.|
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## [sklearn.exceptions: Exceptions and warnings](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.exceptions)
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@ -234,158 +192,75 @@ see [here](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.e
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feature_extraction.DictVectorizer(*[, ...])
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Transforms lists of feature-value mappings to vectors.
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feature_extraction.FeatureHasher([...])
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Implements feature hashing, aka the hashing trick.
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|feature_extraction.DictVectorizer(*[, ...])|Transforms lists of feature-value mappings to vectors.|
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|feature_extraction.FeatureHasher([...])|Implements feature hashing, aka the hashing trick.|
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### From images
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feature_extraction.image.extract_patches_2d(...)
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Reshape a 2D image into a collection of patches.
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feature_extraction.image.grid_to_graph(n_x, n_y)
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Graph of the pixel-to-pixel connections.
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feature_extraction.image.img_to_graph(img, *)
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Graph of the pixel-to-pixel gradient connections.
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feature_extraction.image.reconstruct_from_patches_2d(...)
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Reconstruct the image from all of its patches.
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feature_extraction.image.PatchExtractor(*[, ...])
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Extracts patches from a collection of images.
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|feature_extraction.image.extract_patches_2d(...)|Reshape a 2D image into a collection of patches.|
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|feature_extraction.image.grid_to_graph(n_x, n_y)|Graph of the pixel-to-pixel connections.|
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|feature_extraction.image.img_to_graph(img, *)|Graph of the pixel-to-pixel gradient connections.|
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|feature_extraction.image.reconstruct_from_patches_2d(...)|Reconstruct the image from all of its patches.|
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|feature_extraction.image.PatchExtractor(*[, ...])|Extracts patches from a collection of images.|
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### From text
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feature_extraction.text.CountVectorizer(*[, ...])
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Convert a collection of text documents to a matrix of token counts.
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feature_extraction.text.HashingVectorizer(*)
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Convert a collection of text documents to a matrix of token occurrences.
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feature_extraction.text.TfidfTransformer(*)
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Transform a count matrix to a normalized tf or tf-idf representation.
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feature_extraction.text.TfidfVectorizer(*[, ...])
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Convert a collection of raw documents to a matrix of TF-IDF features.
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|feature_extraction.text.CountVectorizer(*[, ...])|Convert a collection of text documents to a matrix of token counts.|
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|feature_extraction.text.HashingVectorizer(*)|Convert a collection of text documents to a matrix of token occurrences.|
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|feature_extraction.text.TfidfTransformer(*)|Transform a count matrix to a normalized tf or tf-idf representation.|
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|feature_extraction.text.TfidfVectorizer(*[, ...])|Convert a collection of raw documents to a matrix of TF-IDF features.|
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## [sklearn.feature_selection: Feature Selection](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.feature_selection)
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feature_selection.GenericUnivariateSelect([...])
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Univariate feature selector with configurable strategy.
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feature_selection.SelectPercentile([...])
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Select features according to a percentile of the highest scores.
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feature_selection.SelectKBest([score_func, k])
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Select features according to the k highest scores.
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feature_selection.SelectFpr([score_func, alpha])
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Filter: Select the pvalues below alpha based on a FPR test.
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feature_selection.SelectFdr([score_func, alpha])
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Filter: Select the p-values for an estimated false discovery rate.
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feature_selection.SelectFromModel(estimator, *)
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Meta-transformer for selecting features based on importance weights.
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feature_selection.SelectFwe([score_func, alpha])
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Filter: Select the p-values corresponding to Family-wise error rate.
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feature_selection.SequentialFeatureSelector(...)
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Transformer that performs Sequential Feature Selection.
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feature_selection.RFE(estimator, *[, ...])
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Feature ranking with recursive feature elimination.
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feature_selection.RFECV(estimator, *[, ...])
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Recursive feature elimination with cross-validation to select features.
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feature_selection.VarianceThreshold([threshold])
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Feature selector that removes all low-variance features.
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feature_selection.chi2(X, y)
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Compute chi-squared stats between each non-negative feature and class.
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feature_selection.f_classif(X, y)
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Compute the ANOVA F-value for the provided sample.
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feature_selection.f_regression(X, y, *[, ...])
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Univariate linear regression tests returning F-statistic and p-values.
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feature_selection.r_regression(X, y, *[, ...])
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Compute Pearson's r for each features and the target.
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feature_selection.mutual_info_classif(X, y, *)
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Estimate mutual information for a discrete target variable.
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feature_selection.mutual_info_regression(X, y, *)
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Estimate mutual information for a continuous target variable.
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|feature_selection.GenericUnivariateSelect([...])|Univariate feature selector with configurable strategy.|
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|feature_selection.SelectPercentile([...])|Select features according to a percentile of the highest scores.|
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|feature_selection.SelectKBest([score_func, k])|Select features according to the k highest scores.|
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|feature_selection.SelectFpr([score_func, alpha])|Filter: Select the pvalues below alpha based on a FPR test.|
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|feature_selection.SelectFdr([score_func, alpha])|Filter: Select the p-values for an estimated false discovery rate.|
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|feature_selection.SelectFromModel(estimator, *)|Meta-transformer for selecting features based on importance weights.|
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|feature_selection.SelectFwe([score_func, alpha])|Filter: Select the p-values corresponding to Family-wise error rate.|
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|feature_selection.SequentialFeatureSelector(...)|Transformer that performs Sequential Feature Selection.|
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|feature_selection.RFE(estimator, *[, ...])|Feature ranking with recursive feature elimination.|
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|feature_selection.RFECV(estimator, *[, ...])|Recursive feature elimination with cross-validation to select features.|
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|feature_selection.VarianceThreshold([threshold])|Feature selector that removes all low-variance features.|
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|feature_selection.chi2(X, y)|Compute chi-squared stats between each non-negative feature and class.|
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|feature_selection.f_classif(X, y)|Compute the ANOVA F-value for the provided sample.|
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|feature_selection.f_regression(X, y, *[, ...])|Univariate linear regression tests returning F-statistic and p-values.|
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|feature_selection.r_regression(X, y, *[, ...])|Compute Pearson's r for each features and the target.|
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|feature_selection.mutual_info_classif(X, y, *)|Estimate mutual information for a discrete target variable.|
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|feature_selection.mutual_info_regression(X, y, *)|Estimate mutual information for a continuous target variable.|
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## [sklearn.gaussian_process: Gaussian Processes]()
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gaussian_process.GaussianProcessClassifier([...])
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Gaussian process classification (GPC) based on Laplace approximation.
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gaussian_process.GaussianProcessRegressor([...])
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Gaussian process regression (GPR).
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|gaussian_process.GaussianProcessClassifier([...])|Gaussian process classification (GPC) based on Laplace approximation.|
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|gaussian_process.GaussianProcessRegressor([...])|Gaussian process regression (GPR).|
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### Kernels
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gaussian_process.kernels.CompoundKernel(kernels)
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Kernel which is composed of a set of other kernels.
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gaussian_process.kernels.ConstantKernel([...])
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Constant kernel.
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gaussian_process.kernels.DotProduct([...])
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Dot-Product kernel.
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gaussian_process.kernels.ExpSineSquared([...])
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Exp-Sine-Squared kernel (aka periodic kernel).
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gaussian_process.kernels.Exponentiation(...)
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The Exponentiation kernel takes one base kernel and a scalar parameter and combines them via
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gaussian_process.kernels.Hyperparameter(...)
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A kernel hyperparameter's specification in form of a namedtuple.
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gaussian_process.kernels.Kernel()
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Base class for all kernels.
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gaussian_process.kernels.Matern([...])
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Matern kernel.
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gaussian_process.kernels.PairwiseKernel([...])
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Wrapper for kernels in sklearn.metrics.pairwise.
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gaussian_process.kernels.Product(k1, k2)
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The Product kernel takes two kernels k1 and k2 and combines them via
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gaussian_process.kernels.RBF([length_scale, ...])
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Radial basis function kernel (aka squared-exponential kernel).
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gaussian_process.kernels.RationalQuadratic([...])
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Rational Quadratic kernel.
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gaussian_process.kernels.Sum(k1, k2)
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The Sum kernel takes two kernels k1 and k2 and combines them via
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gaussian_process.kernels.WhiteKernel([...])
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White kernel.
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|gaussian_process.kernels.CompoundKernel(kernels)|Kernel which is composed of a set of other kernels.|
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|gaussian_process.kernels.ConstantKernel([...])|Constant kernel.|
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|gaussian_process.kernels.DotProduct([...])|Dot-Product kernel.|
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|gaussian_process.kernels.ExpSineSquared([...])|Exp-Sine-Squared kernel (aka periodic kernel).|
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|gaussian_process.kernels.Exponentiation(...)|The Exponentiation kernel takes one base kernel and a scalar parameter and combines them via|
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|gaussian_process.kernels.Hyperparameter(...)|A kernel hyperparameter's specification in form of a namedtuple.|
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|gaussian_process.kernels.Kernel()|Base class for all kernels.|
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|gaussian_process.kernels.Matern([...])|Matern kernel.|
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|gaussian_process.kernels.PairwiseKernel([...])|Wrapper for kernels in sklearn.metrics.pairwise.|
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|gaussian_process.kernels.Product(k1, k2)|The Product kernel takes two kernels k1 and k2 and combines them via|
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|gaussian_process.kernels.RBF([length_scale, ...])|Radial basis function kernel (aka squared-exponential kernel).|
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|gaussian_process.kernels.RationalQuadratic([...])|Rational Quadratic kernel.|
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|gaussian_process.kernels.Sum(k1, k2)|The Sum kernel takes two kernels k1 and k2 and combines them via|
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|gaussian_process.kernels.WhiteKernel([...])|White kernel.|
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## [sklearn.impute: Impute](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.impute)
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