diff --git a/scikit-learn/overview/README.md b/scikit-learn/overview/README.md index 1ccad3d..58d7be9 100644 --- a/scikit-learn/overview/README.md +++ b/scikit-learn/overview/README.md @@ -266,78 +266,49 @@ see [here](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.e ||| |---|---| -impute.SimpleImputer(*[, missing_values, ...]) -Univariate imputer for completing missing values with simple strategies. - -impute.IterativeImputer([estimator, ...]) -Multivariate imputer that estimates each feature from all the others. - -impute.MissingIndicator(*[, missing_values, ...]) -Binary indicators for missing values. - -impute.KNNImputer(*[, missing_values, ...]) -Imputation for completing missing values using k-Nearest Neighbors. +|impute.SimpleImputer(*[, missing_values, ...])|Univariate imputer for completing missing values with simple strategies.| +|impute.IterativeImputer([estimator, ...])|Multivariate imputer that estimates each feature from all the others.| +|impute.MissingIndicator(*[, missing_values, ...])|Binary indicators for missing values.| +|impute.KNNImputer(*[, missing_values, ...])|Imputation for completing missing values using k-Nearest Neighbors.| ## [sklearn.inspection: Inspection](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.inspection) ||| |---|---| -inspection.partial_dependence(estimator, X, ...) -Partial dependence of features. - -inspection.permutation_importance(estimator, ...) -Permutation importance for feature evaluation [Rd9e56ef97513-BRE]. +|inspection.partial_dependence(estimator, X, ...)|Partial dependence of features.| +|inspection.permutation_importance(estimator, ...)|Permutation importance for feature evaluation [Rd9e56ef97513-BRE].| ### Plotting ||| |---|---| -inspection.DecisionBoundaryDisplay(*, xx0, ...) -Decisions boundary visualization. - -inspection.PartialDependenceDisplay(...[, ...]) -Partial Dependence Plot (PDP). - +|inspection.DecisionBoundaryDisplay(*, xx0, ...)|Decisions boundary visualization.| +|inspection.PartialDependenceDisplay(...[, ...])|Partial Dependence Plot (PDP).| ## [sklearn.isotonic: Isotonic regression](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.isotonic) ||| |---|---| -isotonic.IsotonicRegression(*[, y_min, ...]) -Isotonic regression model. - -isotonic.check_increasing(x, y) -Determine whether y is monotonically correlated with x. - -isotonic.isotonic_regression(y, *[, ...]) -Solve the isotonic regression model. +|isotonic.IsotonicRegression(*[, y_min, ...])|Isotonic regression model.| +|isotonic.check_increasing(x, y)|Determine whether y is monotonically correlated with x.| +|isotonic.isotonic_regression(y, *[, ...])|Solve the isotonic regression model.| ## [sklearn.kernel_approximation: Kernel Approximation](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.kernel_approximation) ||| |---|---| -kernel_approximation.AdditiveChi2Sampler(*) -Approximate feature map for additive chi2 kernel. - -kernel_approximation.Nystroem([kernel, ...]) -Approximate a kernel map using a subset of the training data. - -kernel_approximation.PolynomialCountSketch(*) -Polynomial kernel approximation via Tensor Sketch. - -kernel_approximation.RBFSampler(*[, gamma, ...]) -Approximate a RBF kernel feature map using random Fourier features. - -kernel_approximation.SkewedChi2Sampler(*[, ...]) -Approximate feature map for "skewed chi-squared" kernel. +|kernel_approximation.AdditiveChi2Sampler(*)|Approximate feature map for additive chi2 kernel.| +|kernel_approximation.Nystroem([kernel, ...])|Approximate a kernel map using a subset of the training data.| +|kernel_approximation.PolynomialCountSketch(*)|Polynomial kernel approximation via Tensor Sketch.| +|kernel_approximation.RBFSampler(*[, gamma, ...])|Approximate a RBF kernel feature map using random Fourier features.| +|kernel_approximation.SkewedChi2Sampler(*[, ...])|Approximate feature map for "skewed chi-squared" kernel.| ## [sklearn.kernel_ridge: Kernel Ridge Regression](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.kernel_ridge) ||| |---|---| -kernel_ridge.KernelRidge([alpha, kernel, ...]) -Kernel ridge regression. +|kernel_ridge.KernelRidge([alpha, kernel, ...])|Kernel ridge regression.| ## [sklearn.linear_model: Linear Models](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.linear_model) @@ -345,198 +316,100 @@ Kernel ridge regression. ||| |---|---| -linear_model.LogisticRegression([penalty, ...]) -Logistic Regression (aka logit, MaxEnt) classifier. - -linear_model.LogisticRegressionCV(*[, Cs, ...]) -Logistic Regression CV (aka logit, MaxEnt) classifier. - -linear_model.PassiveAggressiveClassifier(*) -Passive Aggressive Classifier. - -linear_model.Perceptron(*[, penalty, alpha, ...]) -Linear perceptron classifier. - -linear_model.RidgeClassifier([alpha, ...]) -Classifier using Ridge regression. - -linear_model.RidgeClassifierCV([alphas, ...]) -Ridge classifier with built-in cross-validation. - -linear_model.SGDClassifier([loss, penalty, ...]) -Linear classifiers (SVM, logistic regression, etc.) with SGD training. - -linear_model.SGDOneClassSVM([nu, ...]) -Solves linear One-Class SVM using Stochastic Gradient Descent. +|linear_model.LogisticRegression([penalty, ...])|Logistic Regression (aka logit, MaxEnt) classifier.| +|linear_model.LogisticRegressionCV(*[, Cs, ...])|Logistic Regression CV (aka logit, MaxEnt) classifier.| +|linear_model.PassiveAggressiveClassifier(*)|Passive Aggressive Classifier.| +|linear_model.Perceptron(*[, penalty, alpha, ...])|Linear perceptron classifier.| +|linear_model.RidgeClassifier([alpha, ...])|Classifier using Ridge regression.| +|linear_model.RidgeClassifierCV([alphas, ...])|Ridge classifier with built-in cross-validation.| +|linear_model.SGDClassifier([loss, penalty, ...])|Linear classifiers (SVM, logistic regression, etc.) with SGD training.| +|linear_model.SGDOneClassSVM([nu, ...])|Solves linear One-Class SVM using Stochastic Gradient Descent.| ### Classical linear regressors ||| |---|---| -linear_model.LinearRegression(*[, ...]) -Ordinary least squares Linear Regression. - -linear_model.Ridge([alpha, fit_intercept, ...]) -Linear least squares with l2 regularization. - -linear_model.RidgeCV([alphas, ...]) -Ridge regression with built-in cross-validation. - -linear_model.SGDRegressor([loss, penalty, ...]) -Linear model fitted by minimizing a regularized empirical loss with SGD. +|linear_model.LinearRegression(*[, ...])|Ordinary least squares Linear Regression.| +|linear_model.Ridge([alpha, fit_intercept, ...])|Linear least squares with l2 regularization.| +|linear_model.RidgeCV([alphas, ...])|Ridge regression with built-in cross-validation.| +|linear_model.SGDRegressor([loss, penalty, ...])|Linear model fitted by minimizing a regularized empirical loss with SGD.| ### Regressors with variable selection ||| |---|---| -linear_model.ElasticNet([alpha, l1_ratio, ...]) -Linear regression with combined L1 and L2 priors as regularizer. - -linear_model.ElasticNetCV(*[, l1_ratio, ...]) -Elastic Net model with iterative fitting along a regularization path. - -linear_model.Lars(*[, fit_intercept, ...]) -Least Angle Regression model a.k.a. - -linear_model.LarsCV(*[, fit_intercept, ...]) -Cross-validated Least Angle Regression model. - -linear_model.Lasso([alpha, fit_intercept, ...]) -Linear Model trained with L1 prior as regularizer (aka the Lasso). - -linear_model.LassoCV(*[, eps, n_alphas, ...]) -Lasso linear model with iterative fitting along a regularization path. - -linear_model.LassoLars([alpha, ...]) -Lasso model fit with Least Angle Regression a.k.a. - -linear_model.LassoLarsCV(*[, fit_intercept, ...]) -Cross-validated Lasso, using the LARS algorithm. - -linear_model.LassoLarsIC([criterion, ...]) -Lasso model fit with Lars using BIC or AIC for model selection. - -linear_model.OrthogonalMatchingPursuit(*[, ...]) -Orthogonal Matching Pursuit model (OMP). - -linear_model.OrthogonalMatchingPursuitCV(*) -Cross-validated Orthogonal Matching Pursuit model (OMP). +|linear_model.ElasticNet([alpha, l1_ratio, ...])|Linear regression with combined L1 and L2 priors as regularizer.| +|linear_model.ElasticNetCV(*[, l1_ratio, ...])|Elastic Net model with iterative fitting along a regularization path.| +|linear_model.Lars(*[, fit_intercept, ...])|Least Angle Regression model a.k.a.| +|linear_model.LarsCV(*[, fit_intercept, ...])|Cross-validated Least Angle Regression model.| +|linear_model.Lasso([alpha, fit_intercept, ...])|Linear Model trained with L1 prior as regularizer (aka the Lasso).| +|linear_model.LassoCV(*[, eps, n_alphas, ...])|Lasso linear model with iterative fitting along a regularization path.| +|linear_model.LassoLars([alpha, ...])|Lasso model fit with Least Angle Regression a.k.a.| +|linear_model.LassoLarsCV(*[, fit_intercept, ...])|Cross-validated Lasso, using the LARS algorithm.| +|linear_model.LassoLarsIC([criterion, ...])|Lasso model fit with Lars using BIC or AIC for model selection.| +|linear_model.OrthogonalMatchingPursuit(*[, ...])|Orthogonal Matching Pursuit model (OMP).| +|linear_model.OrthogonalMatchingPursuitCV(*)|Cross-validated Orthogonal Matching Pursuit model (OMP).| ### Bayesian regressors ||| |---|---| -linear_model.ARDRegression(*[, max_iter, ...]) -Bayesian ARD regression. - -linear_model.BayesianRidge(*[, max_iter, ...]) -Bayesian ridge regression. +|linear_model.ARDRegression(*[, max_iter, ...])|Bayesian ARD regression.| +|linear_model.BayesianRidge(*[, max_iter, ...])|Bayesian ridge regression.| ### Multi-task linear regressors with variable selection ||| |---|---| -linear_model.MultiTaskElasticNet([alpha, ...]) -Multi-task ElasticNet model trained with L1/L2 mixed-norm as regularizer. - -linear_model.MultiTaskElasticNetCV(*[, ...]) -Multi-task L1/L2 ElasticNet with built-in cross-validation. - -linear_model.MultiTaskLasso([alpha, ...]) -Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer. - -linear_model.MultiTaskLassoCV(*[, eps, ...]) -Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer. +|linear_model.MultiTaskElasticNet([alpha, ...])|Multi-task ElasticNet model trained with L1/L2 mixed-norm as regularizer.| +|linear_model.MultiTaskElasticNetCV(*[, ...])|Multi-task L1/L2 ElasticNet with built-in cross-validation.| +|linear_model.MultiTaskLasso([alpha, ...])|Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer.| +|linear_model.MultiTaskLassoCV(*[, eps, ...])|Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer.| ### Outlier-robust regressors ||| |---|---| -linear_model.HuberRegressor(*[, epsilon, ...]) -L2-regularized linear regression model that is robust to outliers. - -linear_model.QuantileRegressor(*[, ...]) -Linear regression model that predicts conditional quantiles. - -linear_model.RANSACRegressor([estimator, ...]) -RANSAC (RANdom SAmple Consensus) algorithm. - -linear_model.TheilSenRegressor(*[, ...]) -Theil-Sen Estimator: robust multivariate regression model. +|linear_model.HuberRegressor(*[, epsilon, ...])|L2-regularized linear regression model that is robust to outliers.| +|linear_model.QuantileRegressor(*[, ...])|Linear regression model that predicts conditional quantiles.| +|linear_model.RANSACRegressor([estimator, ...])|RANSAC (RANdom SAmple Consensus) algorithm.| +|linear_model.TheilSenRegressor(*[, ...])|Theil-Sen Estimator: robust multivariate regression model.| ### Generalized linear models (GLM) for regression ||| |---|---| -linear_model.PoissonRegressor(*[, alpha, ...]) -Generalized Linear Model with a Poisson distribution. - -linear_model.TweedieRegressor(*[, power, ...]) -Generalized Linear Model with a Tweedie distribution. - -linear_model.GammaRegressor(*[, alpha, ...]) -Generalized Linear Model with a Gamma distribution. +|linear_model.PoissonRegressor(*[, alpha, ...])|Generalized Linear Model with a Poisson distribution.| +|linear_model.TweedieRegressor(*[, power, ...])|Generalized Linear Model with a Tweedie distribution.| +|linear_model.GammaRegressor(*[, alpha, ...])|Generalized Linear Model with a Gamma distribution.| ### Miscellaneous ||| |---|---| -linear_model.PassiveAggressiveRegressor(*[, ...]) -Passive Aggressive Regressor. - -linear_model.enet_path(X, y, *[, l1_ratio, ...]) -Compute elastic net path with coordinate descent. - -linear_model.lars_path(X, y[, Xy, Gram, ...]) -Compute Least Angle Regression or Lasso path using the LARS algorithm [1]. - -linear_model.lars_path_gram(Xy, Gram, *, ...) -The lars_path in the sufficient stats mode [1]. - -linear_model.lasso_path(X, y, *[, eps, ...]) -Compute Lasso path with coordinate descent. - -linear_model.orthogonal_mp(X, y, *[, ...]) -Orthogonal Matching Pursuit (OMP). - -linear_model.orthogonal_mp_gram(Gram, Xy, *) -Gram Orthogonal Matching Pursuit (OMP). - -linear_model.ridge_regression(X, y, alpha, *) -Solve the ridge equation by the method of normal equations. +|linear_model.PassiveAggressiveRegressor(*[, ...])|Passive Aggressive Regressor.| +|linear_model.enet_path(X, y, *[, l1_ratio, ...])|Compute elastic net path with coordinate descent.| +|linear_model.lars_path(X, y[, Xy, Gram, ...])|Compute Least Angle Regression or Lasso path using the LARS algorithm [1].| +|linear_model.lars_path_gram(Xy, Gram, *, ...)|The lars_path in the sufficient stats mode [1].| +|linear_model.lasso_path(X, y, *[, eps, ...])|Compute Lasso path with coordinate descent.| +|linear_model.orthogonal_mp(X, y, *[, ...])|Orthogonal Matching Pursuit (OMP).| +|linear_model.orthogonal_mp_gram(Gram, Xy, *)|Gram Orthogonal Matching Pursuit (OMP).| +|linear_model.ridge_regression(X, y, alpha, *)|Solve the ridge equation by the method of normal equations.| ## [sklearn.manifold: Manifold Learning](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.manifold) ||| |---|---| -manifold.Isomap(*[, n_neighbors, radius, ...]) -Isomap Embedding. - -manifold.LocallyLinearEmbedding(*[, ...]) -Locally Linear Embedding. - -manifold.MDS([n_components, metric, n_init, ...]) -Multidimensional scaling. - -manifold.SpectralEmbedding([n_components, ...]) -Spectral embedding for non-linear dimensionality reduction. - -manifold.TSNE([n_components, perplexity, ...]) -T-distributed Stochastic Neighbor Embedding. - -manifold.locally_linear_embedding(X, *, ...) -Perform a Locally Linear Embedding analysis on the data. - -manifold.smacof(dissimilarities, *[, ...]) -Compute multidimensional scaling using the SMACOF algorithm. - -manifold.spectral_embedding(adjacency, *[, ...]) -Project the sample on the first eigenvectors of the graph Laplacian. - -manifold.trustworthiness(X, X_embedded, *[, ...]) -Indicate to what extent the local structure is retained. - +|manifold.Isomap(*[, n_neighbors, radius, ...])|Isomap Embedding.| +|manifold.LocallyLinearEmbedding(*[, ...])|Locally Linear Embedding.| +|manifold.MDS([n_components, metric, n_init, ...])|Multidimensional scaling.| +|manifold.SpectralEmbedding([n_components, ...])|Spectral embedding for non-linear dimensionality reduction.| +|manifold.TSNE([n_components, perplexity, ...])|T-distributed Stochastic Neighbor Embedding.| +|manifold.locally_linear_embedding(X, *, ...)|Perform a Locally Linear Embedding analysis on the data.| +|manifold.smacof(dissimilarities, *[, ...])|Compute multidimensional scaling using the SMACOF algorithm.| +|manifold.spectral_embedding(adjacency, *[, ...])|Project the sample on the first eigenvectors of the graph Laplacian.| +|manifold.trustworthiness(X, X_embedded, *[, ...])|Indicate to what extent the local structure is retained.| ## [sklearn.metrics: Metrics](https://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics) @@ -545,17 +418,10 @@ Indicate to what extent the local structure is retained. ||| |---|---| -metrics.check_scoring(estimator[, scoring, ...]) -Determine scorer from user options. - -metrics.get_scorer(scoring) -Get a scorer from string. - -metrics.get_scorer_names() -Get the names of all available scorers. - -metrics.make_scorer(score_func, *[, ...]) -Make a scorer from a performance metric or loss function. +|metrics.check_scoring(estimator[, scoring, ...])|Determine scorer from user options.| +|metrics.get_scorer(scoring)|Get a scorer from string.| +|metrics.get_scorer_names()|Get the names of all available scorers.| +|metrics.make_scorer(score_func, *[, ...])|Make a scorer from a performance metric or loss function.| ### Classification metrics