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