From 9214c227a3902d7b16ef800ce174bf0edc457667 Mon Sep 17 00:00:00 2001 From: David Rotermund <54365609+davrot@users.noreply.github.com> Date: Sat, 16 Dec 2023 15:13:25 +0100 Subject: [PATCH] Update README.md Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com> --- scikit-learn/overview/README.md | 257 ++++++++------------------------ 1 file changed, 66 insertions(+), 191 deletions(-) diff --git a/scikit-learn/overview/README.md b/scikit-learn/overview/README.md index 1e60af6..10dbe9f 100644 --- a/scikit-learn/overview/README.md +++ b/scikit-learn/overview/README.md @@ -147,80 +147,38 @@ see [here](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.d ||| |---|---| -discriminant_analysis.LinearDiscriminantAnalysis([...]) -Linear Discriminant Analysis. - -discriminant_analysis.QuadraticDiscriminantAnalysis(*) -Quadratic Discriminant Analysis. - +|discriminant_analysis.LinearDiscriminantAnalysis([...])|Linear Discriminant Analysis.| +|discriminant_analysis.QuadraticDiscriminantAnalysis(*)|Quadratic Discriminant Analysis.| ## [sklearn.dummy: Dummy estimators](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.dummy) ||| |---|---| -dummy.DummyClassifier(*[, strategy, ...]) -DummyClassifier makes predictions that ignore the input features. - -dummy.DummyRegressor(*[, strategy, ...]) -Regressor that makes predictions using simple rules. +|dummy.DummyClassifier(*[, strategy, ...])|DummyClassifier makes predictions that ignore the input features.| +|dummy.DummyRegressor(*[, strategy, ...])|Regressor that makes predictions using simple rules.| ## [sklearn.ensemble: Ensemble Methods](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.ensemble) ||| |---|---| -ensemble.AdaBoostClassifier([estimator, ...]) -An AdaBoost classifier. - -ensemble.AdaBoostRegressor([estimator, ...]) -An AdaBoost regressor. - -ensemble.BaggingClassifier([estimator, ...]) -A Bagging classifier. - -ensemble.BaggingRegressor([estimator, ...]) -A Bagging regressor. - -ensemble.ExtraTreesClassifier([...]) -An extra-trees classifier. - -ensemble.ExtraTreesRegressor([n_estimators, ...]) -An extra-trees regressor. - -ensemble.GradientBoostingClassifier(*[, ...]) -Gradient Boosting for classification. - -ensemble.GradientBoostingRegressor(*[, ...]) -Gradient Boosting for regression. - -ensemble.IsolationForest(*[, n_estimators, ...]) -Isolation Forest Algorithm. - -ensemble.RandomForestClassifier([...]) -A random forest classifier. - -ensemble.RandomForestRegressor([...]) -A random forest regressor. - -ensemble.RandomTreesEmbedding([...]) -An ensemble of totally random trees. - -ensemble.StackingClassifier(estimators[, ...]) -Stack of estimators with a final classifier. - -ensemble.StackingRegressor(estimators[, ...]) -Stack of estimators with a final regressor. - -ensemble.VotingClassifier(estimators, *[, ...]) -Soft Voting/Majority Rule classifier for unfitted estimators. - -ensemble.VotingRegressor(estimators, *[, ...]) -Prediction voting regressor for unfitted estimators. - -ensemble.HistGradientBoostingRegressor([...]) -Histogram-based Gradient Boosting Regression Tree. - -ensemble.HistGradientBoostingClassifier([...]) -Histogram-based Gradient Boosting Classification Tree. +|ensemble.AdaBoostClassifier([estimator, ...])|An AdaBoost classifier.| +|ensemble.AdaBoostRegressor([estimator, ...])|An AdaBoost regressor.| +|ensemble.BaggingClassifier([estimator, ...])|A Bagging classifier.| +|ensemble.BaggingRegressor([estimator, ...])|A Bagging regressor.| +|ensemble.ExtraTreesClassifier([...])|An extra-trees classifier.| +|ensemble.ExtraTreesRegressor([n_estimators, ...])|An extra-trees regressor.| +|ensemble.GradientBoostingClassifier(*[, ...])|Gradient Boosting for classification.| +|ensemble.GradientBoostingRegressor(*[, ...])|Gradient Boosting for regression.| +|ensemble.IsolationForest(*[, n_estimators, ...])|Isolation Forest Algorithm.| +|ensemble.RandomForestClassifier([...])|A random forest classifier.| +|ensemble.RandomForestRegressor([...])|A random forest regressor.| +|ensemble.RandomTreesEmbedding([...])|An ensemble of totally random trees.| +|ensemble.StackingClassifier(estimators[, ...])|Stack of estimators with a final classifier.| +|ensemble.StackingRegressor(estimators[, ...])|Stack of estimators with a final regressor.| +|ensemble.VotingClassifier(estimators, *[, ...])|Soft Voting/Majority Rule classifier for unfitted estimators.| +|ensemble.VotingRegressor(estimators, *[, ...])|Prediction voting regressor for unfitted estimators.| +|ensemble.HistGradientBoostingRegressor([...])|Histogram-based Gradient Boosting Regression Tree.| +|ensemble.HistGradientBoostingClassifier([...])|Histogram-based Gradient Boosting Classification Tree.| ## [sklearn.exceptions: Exceptions and warnings](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.exceptions) @@ -234,158 +192,75 @@ see [here](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.e ||| |---|---| -feature_extraction.DictVectorizer(*[, ...]) -Transforms lists of feature-value mappings to vectors. - -feature_extraction.FeatureHasher([...]) -Implements feature hashing, aka the hashing trick. +|feature_extraction.DictVectorizer(*[, ...])|Transforms lists of feature-value mappings to vectors.| +|feature_extraction.FeatureHasher([...])|Implements feature hashing, aka the hashing trick.| ### From images ||| |---|---| -feature_extraction.image.extract_patches_2d(...) -Reshape a 2D image into a collection of patches. - -feature_extraction.image.grid_to_graph(n_x, n_y) -Graph of the pixel-to-pixel connections. - -feature_extraction.image.img_to_graph(img, *) -Graph of the pixel-to-pixel gradient connections. - -feature_extraction.image.reconstruct_from_patches_2d(...) -Reconstruct the image from all of its patches. - -feature_extraction.image.PatchExtractor(*[, ...]) -Extracts patches from a collection of images. +|feature_extraction.image.extract_patches_2d(...)|Reshape a 2D image into a collection of patches.| +|feature_extraction.image.grid_to_graph(n_x, n_y)|Graph of the pixel-to-pixel connections.| +|feature_extraction.image.img_to_graph(img, *)|Graph of the pixel-to-pixel gradient connections.| +|feature_extraction.image.reconstruct_from_patches_2d(...)|Reconstruct the image from all of its patches.| +|feature_extraction.image.PatchExtractor(*[, ...])|Extracts patches from a collection of images.| ### From text ||| |---|---| -feature_extraction.text.CountVectorizer(*[, ...]) -Convert a collection of text documents to a matrix of token counts. - -feature_extraction.text.HashingVectorizer(*) -Convert a collection of text documents to a matrix of token occurrences. - -feature_extraction.text.TfidfTransformer(*) -Transform a count matrix to a normalized tf or tf-idf representation. - -feature_extraction.text.TfidfVectorizer(*[, ...]) -Convert a collection of raw documents to a matrix of TF-IDF features. +|feature_extraction.text.CountVectorizer(*[, ...])|Convert a collection of text documents to a matrix of token counts.| +|feature_extraction.text.HashingVectorizer(*)|Convert a collection of text documents to a matrix of token occurrences.| +|feature_extraction.text.TfidfTransformer(*)|Transform a count matrix to a normalized tf or tf-idf representation.| +|feature_extraction.text.TfidfVectorizer(*[, ...])|Convert a collection of raw documents to a matrix of TF-IDF features.| ## [sklearn.feature_selection: Feature Selection](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.feature_selection) ||| |---|---| -feature_selection.GenericUnivariateSelect([...]) -Univariate feature selector with configurable strategy. - -feature_selection.SelectPercentile([...]) -Select features according to a percentile of the highest scores. - -feature_selection.SelectKBest([score_func, k]) -Select features according to the k highest scores. - -feature_selection.SelectFpr([score_func, alpha]) -Filter: Select the pvalues below alpha based on a FPR test. - -feature_selection.SelectFdr([score_func, alpha]) -Filter: Select the p-values for an estimated false discovery rate. - -feature_selection.SelectFromModel(estimator, *) -Meta-transformer for selecting features based on importance weights. - -feature_selection.SelectFwe([score_func, alpha]) -Filter: Select the p-values corresponding to Family-wise error rate. - -feature_selection.SequentialFeatureSelector(...) -Transformer that performs Sequential Feature Selection. - -feature_selection.RFE(estimator, *[, ...]) -Feature ranking with recursive feature elimination. - -feature_selection.RFECV(estimator, *[, ...]) -Recursive feature elimination with cross-validation to select features. - -feature_selection.VarianceThreshold([threshold]) -Feature selector that removes all low-variance features. - -feature_selection.chi2(X, y) -Compute chi-squared stats between each non-negative feature and class. - -feature_selection.f_classif(X, y) -Compute the ANOVA F-value for the provided sample. - -feature_selection.f_regression(X, y, *[, ...]) -Univariate linear regression tests returning F-statistic and p-values. - -feature_selection.r_regression(X, y, *[, ...]) -Compute Pearson's r for each features and the target. - -feature_selection.mutual_info_classif(X, y, *) -Estimate mutual information for a discrete target variable. - -feature_selection.mutual_info_regression(X, y, *) -Estimate mutual information for a continuous target variable. +|feature_selection.GenericUnivariateSelect([...])|Univariate feature selector with configurable strategy.| +|feature_selection.SelectPercentile([...])|Select features according to a percentile of the highest scores.| +|feature_selection.SelectKBest([score_func, k])|Select features according to the k highest scores.| +|feature_selection.SelectFpr([score_func, alpha])|Filter: Select the pvalues below alpha based on a FPR test.| +|feature_selection.SelectFdr([score_func, alpha])|Filter: Select the p-values for an estimated false discovery rate.| +|feature_selection.SelectFromModel(estimator, *)|Meta-transformer for selecting features based on importance weights.| +|feature_selection.SelectFwe([score_func, alpha])|Filter: Select the p-values corresponding to Family-wise error rate.| +|feature_selection.SequentialFeatureSelector(...)|Transformer that performs Sequential Feature Selection.| +|feature_selection.RFE(estimator, *[, ...])|Feature ranking with recursive feature elimination.| +|feature_selection.RFECV(estimator, *[, ...])|Recursive feature elimination with cross-validation to select features.| +|feature_selection.VarianceThreshold([threshold])|Feature selector that removes all low-variance features.| +|feature_selection.chi2(X, y)|Compute chi-squared stats between each non-negative feature and class.| +|feature_selection.f_classif(X, y)|Compute the ANOVA F-value for the provided sample.| +|feature_selection.f_regression(X, y, *[, ...])|Univariate linear regression tests returning F-statistic and p-values.| +|feature_selection.r_regression(X, y, *[, ...])|Compute Pearson's r for each features and the target.| +|feature_selection.mutual_info_classif(X, y, *)|Estimate mutual information for a discrete target variable.| +|feature_selection.mutual_info_regression(X, y, *)|Estimate mutual information for a continuous target variable.| ## [sklearn.gaussian_process: Gaussian Processes]() ||| |---|---| -gaussian_process.GaussianProcessClassifier([...]) -Gaussian process classification (GPC) based on Laplace approximation. - -gaussian_process.GaussianProcessRegressor([...]) -Gaussian process regression (GPR). - +|gaussian_process.GaussianProcessClassifier([...])|Gaussian process classification (GPC) based on Laplace approximation.| +|gaussian_process.GaussianProcessRegressor([...])|Gaussian process regression (GPR).| ### Kernels ||| |---|---| -gaussian_process.kernels.CompoundKernel(kernels) -Kernel which is composed of a set of other kernels. - -gaussian_process.kernels.ConstantKernel([...]) -Constant kernel. - -gaussian_process.kernels.DotProduct([...]) -Dot-Product kernel. - -gaussian_process.kernels.ExpSineSquared([...]) -Exp-Sine-Squared kernel (aka periodic kernel). - -gaussian_process.kernels.Exponentiation(...) -The Exponentiation kernel takes one base kernel and a scalar parameter and combines them via - -gaussian_process.kernels.Hyperparameter(...) -A kernel hyperparameter's specification in form of a namedtuple. - -gaussian_process.kernels.Kernel() -Base class for all kernels. - -gaussian_process.kernels.Matern([...]) -Matern kernel. - -gaussian_process.kernels.PairwiseKernel([...]) -Wrapper for kernels in sklearn.metrics.pairwise. - -gaussian_process.kernels.Product(k1, k2) -The Product kernel takes two kernels k1 and k2 and combines them via - -gaussian_process.kernels.RBF([length_scale, ...]) -Radial basis function kernel (aka squared-exponential kernel). - -gaussian_process.kernels.RationalQuadratic([...]) -Rational Quadratic kernel. - -gaussian_process.kernels.Sum(k1, k2) -The Sum kernel takes two kernels k1 and k2 and combines them via - -gaussian_process.kernels.WhiteKernel([...]) -White kernel. +|gaussian_process.kernels.CompoundKernel(kernels)|Kernel which is composed of a set of other kernels.| +|gaussian_process.kernels.ConstantKernel([...])|Constant kernel.| +|gaussian_process.kernels.DotProduct([...])|Dot-Product kernel.| +|gaussian_process.kernels.ExpSineSquared([...])|Exp-Sine-Squared kernel (aka periodic kernel).| +|gaussian_process.kernels.Exponentiation(...)|The Exponentiation kernel takes one base kernel and a scalar parameter and combines them via| +|gaussian_process.kernels.Hyperparameter(...)|A kernel hyperparameter's specification in form of a namedtuple.| +|gaussian_process.kernels.Kernel()|Base class for all kernels.| +|gaussian_process.kernels.Matern([...])|Matern kernel.| +|gaussian_process.kernels.PairwiseKernel([...])|Wrapper for kernels in sklearn.metrics.pairwise.| +|gaussian_process.kernels.Product(k1, k2)|The Product kernel takes two kernels k1 and k2 and combines them via| +|gaussian_process.kernels.RBF([length_scale, ...])|Radial basis function kernel (aka squared-exponential kernel).| +|gaussian_process.kernels.RationalQuadratic([...])|Rational Quadratic kernel.| +|gaussian_process.kernels.Sum(k1, k2)|The Sum kernel takes two kernels k1 and k2 and combines them via| +|gaussian_process.kernels.WhiteKernel([...])|White kernel.| ## [sklearn.impute: Impute](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.impute)