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