diff --git a/scikit-learn/overview/README.md b/scikit-learn/overview/README.md index 19650c9..2bc81e2 100644 --- a/scikit-learn/overview/README.md +++ b/scikit-learn/overview/README.md @@ -32,12 +32,10 @@ see [here](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.b ## [sklearn.calibration: Probability Calibration](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.calibration) - -calibration.CalibratedClassifierCV([...]) -Probability calibration with isotonic regression or logistic regression. - -calibration.calibration_curve(y_true, y_prob, *) -Compute true and predicted probabilities for a calibration curve. +||| +|---|---| +|calibration.CalibratedClassifierCV([...])|Probability calibration with isotonic regression or logistic regression.| +|calibration.calibration_curve(y_true, y_prob, *)|Compute true and predicted probabilities for a calibration curve.| ## [sklearn.cluster: Clustering](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.cluster) @@ -45,86 +43,43 @@ Compute true and predicted probabilities for a calibration curve. ### Classes -cluster.AffinityPropagation(*[, damping, ...]) -Perform Affinity Propagation Clustering of data. - -cluster.AgglomerativeClustering([...]) -Agglomerative Clustering. - -cluster.Birch(*[, threshold, ...]) -Implements the BIRCH clustering algorithm. - -cluster.DBSCAN([eps, min_samples, metric, ...]) -Perform DBSCAN clustering from vector array or distance matrix. - -cluster.HDBSCAN([min_cluster_size, ...]) -Cluster data using hierarchical density-based clustering. - -cluster.FeatureAgglomeration([n_clusters, ...]) -Agglomerate features. - -**cluster.KMeans([n_clusters, init, n_init, ...])** -**K-Means clustering.** - -cluster.BisectingKMeans([n_clusters, init, ...]) -Bisecting K-Means clustering. - -**cluster.MiniBatchKMeans([n_clusters, init, ...])** -**Mini-Batch K-Means clustering.** - -cluster.MeanShift(*[, bandwidth, seeds, ...]) -Mean shift clustering using a flat kernel. - -cluster.OPTICS(*[, min_samples, max_eps, ...]) -Estimate clustering structure from vector array. - -cluster.SpectralClustering([n_clusters, ...]) -Apply clustering to a projection of the normalized Laplacian. - -cluster.SpectralBiclustering([n_clusters, ...]) -Spectral biclustering (Kluger, 2003). - -cluster.SpectralCoclustering([n_clusters, ...]) -Spectral Co-Clustering algorithm (Dhillon, 2001). +||| +|---|---| +|cluster.AffinityPropagation(*[, damping, ...])|Perform Affinity Propagation Clustering of data.| +|cluster.AgglomerativeClustering([...])|Agglomerative Clustering.| +|cluster.Birch(*[, threshold, ...])|Implements the BIRCH clustering algorithm.| +|cluster.DBSCAN([eps, min_samples, metric, ...])|Perform DBSCAN clustering from vector array or distance matrix.| +|cluster.HDBSCAN([min_cluster_size, ...])|Cluster data using hierarchical density-based clustering.| +|cluster.FeatureAgglomeration([n_clusters, ...])|Agglomerate features.| +|**cluster.KMeans([n_clusters, init, n_init, ...])**|**K-Means clustering.**| +|cluster.BisectingKMeans([n_clusters, init, ...])|Bisecting K-Means clustering.| +|**cluster.MiniBatchKMeans([n_clusters, init, ...])**|**Mini-Batch K-Means clustering.**| +|cluster.MeanShift(*[, bandwidth, seeds, ...])|Mean shift clustering using a flat kernel.| +|cluster.OPTICS(*[, min_samples, max_eps, ...])|Estimate clustering structure from vector array.| +|cluster.SpectralClustering([n_clusters, ...])|Apply clustering to a projection of the normalized Laplacian.| +|cluster.SpectralBiclustering([n_clusters, ...])|Spectral biclustering (Kluger, 2003).| +|cluster.SpectralCoclustering([n_clusters, ...])|Spectral Co-Clustering algorithm (Dhillon, 2001).| ### Functions -cluster.affinity_propagation(S, *[, ...]) -Perform Affinity Propagation Clustering of data. - -cluster.cluster_optics_dbscan(*, ...) -Perform DBSCAN extraction for an arbitrary epsilon. - -cluster.cluster_optics_xi(*, reachability, ...) -Automatically extract clusters according to the Xi-steep method. - -cluster.compute_optics_graph(X, *, ...) -Compute the OPTICS reachability graph. - -cluster.dbscan(X[, eps, min_samples, ...]) -Perform DBSCAN clustering from vector array or distance matrix. - -cluster.estimate_bandwidth(X, *[, quantile, ...]) -Estimate the bandwidth to use with the mean-shift algorithm. - -cluster.k_means(X, n_clusters, *[, ...]) -Perform K-means clustering algorithm. - -cluster.kmeans_plusplus(X, n_clusters, *[, ...]) -Init n_clusters seeds according to k-means++. - -cluster.mean_shift(X, *[, bandwidth, seeds, ...]) -Perform mean shift clustering of data using a flat kernel. - -cluster.spectral_clustering(affinity, *[, ...]) -Apply clustering to a projection of the normalized Laplacian. - -cluster.ward_tree(X, *[, connectivity, ...]) -Ward clustering based on a Feature matrix. - +||| +|---|---| +|cluster.affinity_propagation(S, *[, ...])|Perform Affinity Propagation Clustering of data.| +|cluster.cluster_optics_dbscan(*, ...)|Perform DBSCAN extraction for an arbitrary epsilon.| +|cluster.cluster_optics_xi(*, reachability, ...)|Automatically extract clusters according to the Xi-steep method.| +|cluster.compute_optics_graph(X, *, ...)|Compute the OPTICS reachability graph.| +|cluster.dbscan(X[, eps, min_samples, ...])|Perform DBSCAN clustering from vector array or distance matrix.| +|cluster.estimate_bandwidth(X, *[, quantile, ...])|Estimate the bandwidth to use with the mean-shift algorithm.| +|cluster.k_means(X, n_clusters, *[, ...])|Perform K-means clustering algorithm.| +|cluster.kmeans_plusplus(X, n_clusters, *[, ...])|Init n_clusters seeds according to k-means++.| +|cluster.mean_shift(X, *[, bandwidth, seeds, ...])|Perform mean shift clustering of data using a flat kernel.| +|cluster.spectral_clustering(affinity, *[, ...])|Apply clustering to a projection of the normalized Laplacian.| +|cluster.ward_tree(X, *[, connectivity, ...])|Ward clustering based on a Feature matrix.| ## [sklearn.compose: Composite Estimators](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.compose) +||| +|---|---| compose.ColumnTransformer(transformers, *[, ...]) Applies transformers to columns of an array or pandas DataFrame. @@ -139,6 +94,8 @@ 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. @@ -184,6 +141,8 @@ 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. @@ -205,6 +164,8 @@ see [here](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.d ## [sklearn.decomposition: Matrix Decomposition](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.decomposition) +||| +|---|---| decomposition.DictionaryLearning([...]) Dictionary learning. @@ -264,6 +225,8 @@ Sparse coding. ## [sklearn.discriminant_analysis: Discriminant Analysis](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.discriminant_analysis) +||| +|---|---| discriminant_analysis.LinearDiscriminantAnalysis([...]) Linear Discriminant Analysis. @@ -273,6 +236,8 @@ Quadratic Discriminant Analysis. ## [sklearn.dummy: Dummy estimators](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.dummy) +||| +|---|---| dummy.DummyClassifier(*[, strategy, ...]) DummyClassifier makes predictions that ignore the input features. @@ -281,6 +246,8 @@ Regressor that makes predictions using simple rules. ## [sklearn.ensemble: Ensemble Methods](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.ensemble) +||| +|---|---| ensemble.AdaBoostClassifier([estimator, ...]) An AdaBoost classifier. @@ -345,6 +312,8 @@ see [here](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.e ## [sklearn.feature_extraction: Feature Extraction](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.feature_extraction) +||| +|---|---| feature_extraction.DictVectorizer(*[, ...]) Transforms lists of feature-value mappings to vectors. @@ -353,6 +322,8 @@ Implements feature hashing, aka the hashing trick. ### From images +||| +|---|---| feature_extraction.image.extract_patches_2d(...) Reshape a 2D image into a collection of patches. @@ -370,6 +341,8 @@ Extracts patches from a collection of images. ### From text +||| +|---|---| feature_extraction.text.CountVectorizer(*[, ...]) Convert a collection of text documents to a matrix of token counts. @@ -384,6 +357,8 @@ 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) +||| +|---|---| feature_selection.GenericUnivariateSelect([...]) Univariate feature selector with configurable strategy. @@ -437,6 +412,8 @@ Estimate mutual information for a continuous target variable. ## [sklearn.gaussian_process: Gaussian Processes]() +||| +|---|---| gaussian_process.GaussianProcessClassifier([...]) Gaussian process classification (GPC) based on Laplace approximation. @@ -446,6 +423,8 @@ Gaussian process regression (GPR). ### Kernels +||| +|---|---| gaussian_process.kernels.CompoundKernel(kernels) Kernel which is composed of a set of other kernels. @@ -490,6 +469,8 @@ White kernel. ## [sklearn.impute: Impute](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.impute) +||| +|---|---| impute.SimpleImputer(*[, missing_values, ...]) Univariate imputer for completing missing values with simple strategies. @@ -504,6 +485,8 @@ 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. @@ -512,6 +495,8 @@ Permutation importance for feature evaluation [Rd9e56ef97513-BRE]. ### Plotting +||| +|---|---| inspection.DecisionBoundaryDisplay(*, xx0, ...) Decisions boundary visualization. @@ -522,6 +507,8 @@ 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. @@ -533,6 +520,8 @@ 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. @@ -550,6 +539,8 @@ 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. @@ -557,6 +548,8 @@ Kernel ridge regression. ### Linear classifiers +||| +|---|---| linear_model.LogisticRegression([penalty, ...]) Logistic Regression (aka logit, MaxEnt) classifier. @@ -583,6 +576,8 @@ Solves linear One-Class SVM using Stochastic Gradient Descent. ### Classical linear regressors +||| +|---|---| linear_model.LinearRegression(*[, ...]) Ordinary least squares Linear Regression. @@ -597,7 +592,8 @@ 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. @@ -633,6 +629,8 @@ Cross-validated Orthogonal Matching Pursuit model (OMP). ### Bayesian regressors +||| +|---|---| linear_model.ARDRegression(*[, max_iter, ...]) Bayesian ARD regression. @@ -641,6 +639,8 @@ 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. @@ -656,6 +656,8 @@ 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. @@ -670,6 +672,8 @@ Theil-Sen Estimator: robust multivariate regression model. ### Generalized linear models (GLM) for regression +||| +|---|---| linear_model.PoissonRegressor(*[, alpha, ...]) Generalized Linear Model with a Poisson distribution. @@ -681,6 +685,8 @@ Generalized Linear Model with a Gamma distribution. ### Miscellaneous +||| +|---|---| linear_model.PassiveAggressiveRegressor(*[, ...]) Passive Aggressive Regressor. @@ -707,7 +713,8 @@ 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. @@ -741,6 +748,8 @@ Indicate to what extent the local structure is retained. ### Model Selection Interface +||| +|---|---| metrics.check_scoring(estimator[, scoring, ...]) Determine scorer from user options. @@ -755,6 +764,8 @@ Make a scorer from a performance metric or loss function. ### Classification metrics +||| +|---|---| metrics.accuracy_score(y_true, y_pred, *[, ...]) Accuracy classification score. @@ -841,6 +852,8 @@ Zero-one classification loss. ### Regression metrics +||| +|---|---| metrics.explained_variance_score(y_true, ...) Explained variance regression score function. @@ -889,6 +902,8 @@ D^2 regression score function, fraction of absolute error explained. ### Multilabel ranking metrics +||| +|---|---| metrics.coverage_error(y_true, y_score, *[, ...]) Coverage error measure. @@ -901,6 +916,8 @@ Compute Ranking loss measure. ### Clustering metrics +||| +|---|---| metrics.adjusted_mutual_info_score(...[, ...]) Adjusted Mutual Information between two clusterings. @@ -951,16 +968,22 @@ V-measure cluster labeling given a ground truth. ### Biclustering metrics +||| +|---|---| metrics.consensus_score(a, b, *[, similarity]) The similarity of two sets of biclusters. ### Distance metrics +||| +|---|---| metrics.DistanceMetric Uniform interface for fast distance metric functions. ### Pairwise metrics +||| +|---|---| metrics.pairwise.additive_chi2_kernel(X[, Y]) Compute the additive chi-squared kernel between observations in X and Y. @@ -1035,6 +1058,8 @@ Generate a distance matrix chunk by chunk with optional reduction. ### Plotting +||| +|---|---| metrics.ConfusionMatrixDisplay(...[, ...]) Confusion Matrix visualization. @@ -1057,6 +1082,8 @@ Calibration curve (also known as reliability diagram) visualization. ## [sklearn.mixture: Gaussian Mixture Models](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.mixture) +||| +|---|---| mixture.BayesianGaussianMixture(*[, ...]) Variational Bayesian estimation of a Gaussian mixture. @@ -1068,6 +1095,8 @@ Gaussian Mixture. ### Splitter Classes +||| +|---|---| model_selection.GroupKFold([n_splits]) K-fold iterator variant with non-overlapping groups. @@ -1115,14 +1144,18 @@ Time Series cross-validator ### Splitter Functions +||| +|---|---| model_selection.check_cv([cv, y, classifier]) Input checker utility for building a cross-validator. model_selection.train_test_split(*arrays[, ...]) Split arrays or matrices into random train and test subsets. -##รค Hyper-parameter optimizers +### Hyper-parameter optimizers +||| +|---|---| model_selection.GridSearchCV(estimator, ...) Exhaustive search over specified parameter values for an estimator. @@ -1144,6 +1177,8 @@ Randomized search on hyper parameters. ### Model validation +||| +|---|---| model_selection.cross_validate(estimator, X) Evaluate metric(s) by cross-validation and also record fit/score times. @@ -1165,6 +1200,8 @@ Validation curve. ### Visualization +||| +|---|---| model_selection.LearningCurveDisplay(*, ...) Learning Curve visualization. @@ -1175,6 +1212,8 @@ Validation Curve visualization. ## [sklearn.multiclass: Multiclass classification](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.multiclass) +||| +|---|---| multiclass.OneVsRestClassifier(estimator, *) One-vs-the-rest (OvR) multiclass strategy. @@ -1186,7 +1225,8 @@ multiclass.OutputCodeClassifier(estimator, *) ## [sklearn.multioutput: Multioutput regression and classification](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.multioutput) - +||| +|---|---| multioutput.ClassifierChain(base_estimator, *) A multi-label model that arranges binary classifiers into a chain. @@ -1202,6 +1242,8 @@ A multi-label model that arranges regressions into a chain. ## [sklearn.naive_bayes: Naive Bayes](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.naive_bayes) +||| +|---|---| naive_bayes.BernoulliNB(*[, alpha, ...]) Naive Bayes classifier for multivariate Bernoulli models. @@ -1221,6 +1263,8 @@ Naive Bayes classifier for multinomial models. ## [sklearn.neighbors: Nearest Neighbors](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.neighbors) +||| +|---|---| neighbors.BallTree(X[, leaf_size, metric]) BallTree for fast generalized N-point problems @@ -1272,6 +1316,8 @@ Sort a sparse graph such that each row is stored with increasing values. ## [sklearn.neural_network: Neural network models](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.neural_network) +||| +|---|---| pipeline.FeatureUnion(transformer_list, *[, ...]) Concatenates results of multiple transformer objects. @@ -1292,6 +1338,8 @@ see [here](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.p ## [sklearn.preprocessing: Preprocessing and Normalization](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.preprocessing) +||| +|---|---| preprocessing.Binarizer(*[, threshold, copy]) Binarize data (set feature values to 0 or 1) according to a threshold. @@ -1383,6 +1431,8 @@ Parametric, monotonic transformation to make data more Gaussian-like. ## [sklearn.random_projection: Random projection](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.random_projection) +||| +|---|---| random_projection.GaussianRandomProjection([...]) Reduce dimensionality through Gaussian random projection. @@ -1394,6 +1444,8 @@ Find a 'safe' number of components to randomly project to. ## [sklearn.semi_supervised: Semi-Supervised Learning](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.semi_supervised) +||| +|---|---| semi_supervised.LabelPropagation([kernel, ...]) Label Propagation classifier. @@ -1407,6 +1459,8 @@ Self-training classifier. ## [sklearn.svm: Support Vector Machines](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.svm) +||| +|---|---| svm.LinearSVC([penalty, loss, dual, tol, C, ...]) Linear Support Vector Classification. @@ -1435,6 +1489,8 @@ Return the lowest bound for C. ## [sklearn.tree: Decision Trees](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.tree) +||| +|---|---| tree.DecisionTreeClassifier(*[, criterion, ...]) A decision tree classifier.