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Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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@ -80,81 +80,38 @@ see [here](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.b
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compose.ColumnTransformer(transformers, *[, ...]) |compose.ColumnTransformer(transformers, *[, ...])|Applies transformers to columns of an array or pandas DataFrame.|
Applies transformers to columns of an array or pandas DataFrame. |compose.TransformedTargetRegressor([...])|Meta-estimator to regress on a transformed target.|
|compose.make_column_transformer(*transformers)|Construct a ColumnTransformer from the given transformers.|
compose.TransformedTargetRegressor([...]) |compose.make_column_selector([pattern, ...])|Create a callable to select columns to be used with ColumnTransformer.|
Meta-estimator to regress on a transformed target.
compose.make_column_transformer(*transformers)
Construct a ColumnTransformer from the given transformers.
compose.make_column_selector([pattern, ...])
Create a callable to select columns to be used with ColumnTransformer.
## [sklearn.covariance: Covariance Estimators](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.covariance) ## [sklearn.covariance: Covariance Estimators](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.covariance)
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|---|---| |---|---|
covariance.EmpiricalCovariance(*[, ...]) |covariance.EmpiricalCovariance(*[, ...])|Maximum likelihood covariance estimator.|
Maximum likelihood covariance estimator. |covariance.EllipticEnvelope(*[, ...])|An object for detecting outliers in a Gaussian distributed dataset.|
|covariance.GraphicalLasso([alpha, mode, ...])|Sparse inverse covariance estimation with an l1-penalized estimator.|
covariance.EllipticEnvelope(*[, ...]) |covariance.GraphicalLassoCV(*[, alphas, ...])|Sparse inverse covariance w/ cross-validated choice of the l1 penalty.|
An object for detecting outliers in a Gaussian distributed dataset. |covariance.LedoitWolf(*[, store_precision, ...])|LedoitWolf Estimator.|
|covariance.MinCovDet(*[, store_precision, ...])|Minimum Covariance Determinant (MCD): robust estimator of covariance.|
covariance.GraphicalLasso([alpha, mode, ...]) |covariance.OAS(*[, store_precision, ...])|Oracle Approximating Shrinkage Estimator as proposed in [R69773891e6a6-1].|
Sparse inverse covariance estimation with an l1-penalized estimator. |covariance.ShrunkCovariance(*[, ...])|Covariance estimator with shrinkage.|
|covariance.empirical_covariance(X, *[, ...])|Compute the Maximum likelihood covariance estimator.|
covariance.GraphicalLassoCV(*[, alphas, ...]) |covariance.graphical_lasso(emp_cov, alpha, *)|L1-penalized covariance estimator.|
Sparse inverse covariance w/ cross-validated choice of the l1 penalty. |covariance.ledoit_wolf(X, *[, ...])|Estimate the shrunk Ledoit-Wolf covariance matrix.|
|covariance.ledoit_wolf_shrinkage(X[, ...])|Estimate the shrunk Ledoit-Wolf covariance matrix.|
covariance.LedoitWolf(*[, store_precision, ...]) |covariance.oas(X, *[, assume_centered])|Estimate covariance with the Oracle Approximating Shrinkage as proposed in [Rca3a42e5ec35-1].|
LedoitWolf Estimator. |covariance.shrunk_covariance(emp_cov[, ...])|Calculate a covariance matrix shrunk on the diagonal.|
covariance.MinCovDet(*[, store_precision, ...])
Minimum Covariance Determinant (MCD): robust estimator of covariance.
covariance.OAS(*[, store_precision, ...])
Oracle Approximating Shrinkage Estimator as proposed in [R69773891e6a6-1].
covariance.ShrunkCovariance(*[, ...])
Covariance estimator with shrinkage.
covariance.empirical_covariance(X, *[, ...])
Compute the Maximum likelihood covariance estimator.
covariance.graphical_lasso(emp_cov, alpha, *)
L1-penalized covariance estimator.
covariance.ledoit_wolf(X, *[, ...])
Estimate the shrunk Ledoit-Wolf covariance matrix.
covariance.ledoit_wolf_shrinkage(X[, ...])
Estimate the shrunk Ledoit-Wolf covariance matrix.
covariance.oas(X, *[, assume_centered])
Estimate covariance with the Oracle Approximating Shrinkage as proposed in [Rca3a42e5ec35-1].
covariance.shrunk_covariance(emp_cov[, ...])
Calculate a covariance matrix shrunk on the diagonal.
## [sklearn.cross_decomposition: Cross decomposition](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.cross_decomposition) ## [sklearn.cross_decomposition: Cross decomposition](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.cross_decomposition)
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|---|---| |---|---|
cross_decomposition.CCA([n_components, ...]) |cross_decomposition.CCA([n_components, ...])|Canonical Correlation Analysis, also known as "Mode B" PLS.|
Canonical Correlation Analysis, also known as "Mode B" PLS. |cross_decomposition.PLSCanonical([...])|Partial Least Squares transformer and regressor.|
|cross_decomposition.PLSRegression([...])|PLS regression.|
cross_decomposition.PLSCanonical([...]) |cross_decomposition.PLSSVD([n_components, ...])|Partial Least Square SVD.|
Partial Least Squares transformer and regressor.
cross_decomposition.PLSRegression([...])
PLS regression.
cross_decomposition.PLSSVD([n_components, ...])
Partial Least Square SVD.
## [sklearn.datasets: Datasets](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.datasets) ## [sklearn.datasets: Datasets](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.datasets)
@ -166,62 +123,25 @@ see [here](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.d
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|---|---| |---|---|
decomposition.DictionaryLearning([...]) |decomposition.DictionaryLearning([...])|Dictionary learning.|
Dictionary learning. |decomposition.FactorAnalysis([n_components, ...])|Factor Analysis (FA).|
|**decomposition.FastICA([n_components, ...])**|**FastICA: a fast algorithm for Independent Component Analysis.**|
decomposition.FactorAnalysis([n_components, ...]) |decomposition.IncrementalPCA([n_components, ...])|Incremental principal components analysis (IPCA).|
Factor Analysis (FA). |decomposition.KernelPCA([n_components, ...])|Kernel Principal component analysis (KPCA) [R396fc7d924b8-1].|
|decomposition.LatentDirichletAllocation([...])|Latent Dirichlet Allocation with online variational Bayes algorithm.|
**decomposition.FastICA([n_components, ...])** |decomposition.MiniBatchDictionaryLearning([...])|Mini-batch dictionary learning.|
**FastICA: a fast algorithm for Independent Component Analysis.** |decomposition.MiniBatchSparsePCA([...])|Mini-batch Sparse Principal Components Analysis.|
|decomposition.NMF([n_components, init, ...])|Non-Negative Matrix Factorization (NMF).|
decomposition.IncrementalPCA([n_components, ...]) |decomposition.MiniBatchNMF([n_components, ...])|Mini-Batch Non-Negative Matrix Factorization (NMF).|
Incremental principal components analysis (IPCA). |**decomposition.PCA([n_components, copy, ...])**|**Principal component analysis (PCA).**|
|decomposition.SparsePCA([n_components, ...])|Sparse Principal Components Analysis (SparsePCA).|
decomposition.KernelPCA([n_components, ...]) |decomposition.SparseCoder(dictionary, *[, ...])|Sparse coding.|
Kernel Principal component analysis (KPCA) [R396fc7d924b8-1]. |decomposition.TruncatedSVD([n_components, ...])|Dimensionality reduction using truncated SVD (aka LSA).|
|decomposition.dict_learning(X, n_components, ...)|Solve a dictionary learning matrix factorization problem.|
decomposition.LatentDirichletAllocation([...]) |decomposition.dict_learning_online(X[, ...])|Solve a dictionary learning matrix factorization problem online.|
Latent Dirichlet Allocation with online variational Bayes algorithm. |decomposition.fastica(X[, n_components, ...])|Perform Fast Independent Component Analysis.|
|decomposition.non_negative_factorization(X)|Compute Non-negative Matrix Factorization (NMF).|
decomposition.MiniBatchDictionaryLearning([...]) |decomposition.sparse_encode(X, dictionary, *)|Sparse coding.|
Mini-batch dictionary learning.
decomposition.MiniBatchSparsePCA([...])
Mini-batch Sparse Principal Components Analysis.
decomposition.NMF([n_components, init, ...])
Non-Negative Matrix Factorization (NMF).
decomposition.MiniBatchNMF([n_components, ...])
Mini-Batch Non-Negative Matrix Factorization (NMF).
**decomposition.PCA([n_components, copy, ...])**
**Principal component analysis (PCA).**
decomposition.SparsePCA([n_components, ...])
Sparse Principal Components Analysis (SparsePCA).
decomposition.SparseCoder(dictionary, *[, ...])
Sparse coding.
decomposition.TruncatedSVD([n_components, ...])
Dimensionality reduction using truncated SVD (aka LSA).
decomposition.dict_learning(X, n_components, ...)
Solve a dictionary learning matrix factorization problem.
decomposition.dict_learning_online(X[, ...])
Solve a dictionary learning matrix factorization problem online.
decomposition.fastica(X[, n_components, ...])
Perform Fast Independent Component Analysis.
decomposition.non_negative_factorization(X)
Compute Non-negative Matrix Factorization (NMF).
decomposition.sparse_encode(X, dictionary, *)
Sparse coding.
## [sklearn.discriminant_analysis: Discriminant Analysis](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.discriminant_analysis) ## [sklearn.discriminant_analysis: Discriminant Analysis](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.discriminant_analysis)