diff --git a/scikit-learn/overview/README.md b/scikit-learn/overview/README.md index 2bc81e2..1e60af6 100644 --- a/scikit-learn/overview/README.md +++ b/scikit-learn/overview/README.md @@ -80,81 +80,38 @@ see [here](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.b ||| |---|---| -compose.ColumnTransformer(transformers, *[, ...]) -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.make_column_selector([pattern, ...]) -Create a callable to select columns to be used with ColumnTransformer. +|compose.ColumnTransformer(transformers, *[, ...])|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.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) ||| |---|---| -covariance.EmpiricalCovariance(*[, ...]) -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.GraphicalLassoCV(*[, alphas, ...]) -Sparse inverse covariance w/ cross-validated choice of the l1 penalty. - -covariance.LedoitWolf(*[, store_precision, ...]) -LedoitWolf Estimator. - -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. - +|covariance.EmpiricalCovariance(*[, ...])|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.GraphicalLassoCV(*[, alphas, ...])|Sparse inverse covariance w/ cross-validated choice of the l1 penalty.| +|covariance.LedoitWolf(*[, store_precision, ...])|LedoitWolf Estimator.| +|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) ||| |---|---| -cross_decomposition.CCA([n_components, ...]) -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.PLSSVD([n_components, ...]) -Partial Least Square SVD. - +|cross_decomposition.CCA([n_components, ...])|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.PLSSVD([n_components, ...])|Partial Least Square SVD.| ## [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 ||| |---|---| -decomposition.DictionaryLearning([...]) -Dictionary learning. - -decomposition.FactorAnalysis([n_components, ...]) -Factor Analysis (FA). - -**decomposition.FastICA([n_components, ...])** -**FastICA: a fast algorithm for Independent Component Analysis.** - -decomposition.IncrementalPCA([n_components, ...]) -Incremental principal components analysis (IPCA). - -decomposition.KernelPCA([n_components, ...]) -Kernel Principal component analysis (KPCA) [R396fc7d924b8-1]. - -decomposition.LatentDirichletAllocation([...]) -Latent Dirichlet Allocation with online variational Bayes algorithm. - -decomposition.MiniBatchDictionaryLearning([...]) -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. +|decomposition.DictionaryLearning([...])|Dictionary learning.| +|decomposition.FactorAnalysis([n_components, ...])|Factor Analysis (FA).| +|**decomposition.FastICA([n_components, ...])**|**FastICA: a fast algorithm for Independent Component Analysis.**| +|decomposition.IncrementalPCA([n_components, ...])|Incremental principal components analysis (IPCA).| +|decomposition.KernelPCA([n_components, ...])|Kernel Principal component analysis (KPCA) [R396fc7d924b8-1].| +|decomposition.LatentDirichletAllocation([...])|Latent Dirichlet Allocation with online variational Bayes algorithm.| +|decomposition.MiniBatchDictionaryLearning([...])|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)