Update README.md
Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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@ -32,12 +32,10 @@ see [here](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.b
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## [sklearn.calibration: Probability Calibration](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.calibration)
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calibration.CalibratedClassifierCV([...])
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Probability calibration with isotonic regression or logistic regression.
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calibration.calibration_curve(y_true, y_prob, *)
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Compute true and predicted probabilities for a calibration curve.
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|calibration.CalibratedClassifierCV([...])|Probability calibration with isotonic regression or logistic regression.|
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|calibration.calibration_curve(y_true, y_prob, *)|Compute true and predicted probabilities for a calibration curve.|
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## [sklearn.cluster: Clustering](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.cluster)
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@ -45,86 +43,43 @@ Compute true and predicted probabilities for a calibration curve.
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### Classes
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cluster.AffinityPropagation(*[, damping, ...])
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Perform Affinity Propagation Clustering of data.
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cluster.AgglomerativeClustering([...])
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Agglomerative Clustering.
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cluster.Birch(*[, threshold, ...])
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Implements the BIRCH clustering algorithm.
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cluster.DBSCAN([eps, min_samples, metric, ...])
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Perform DBSCAN clustering from vector array or distance matrix.
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cluster.HDBSCAN([min_cluster_size, ...])
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Cluster data using hierarchical density-based clustering.
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cluster.FeatureAgglomeration([n_clusters, ...])
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Agglomerate features.
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**cluster.KMeans([n_clusters, init, n_init, ...])**
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**K-Means clustering.**
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cluster.BisectingKMeans([n_clusters, init, ...])
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Bisecting K-Means clustering.
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**cluster.MiniBatchKMeans([n_clusters, init, ...])**
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**Mini-Batch K-Means clustering.**
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cluster.MeanShift(*[, bandwidth, seeds, ...])
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Mean shift clustering using a flat kernel.
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cluster.OPTICS(*[, min_samples, max_eps, ...])
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Estimate clustering structure from vector array.
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cluster.SpectralClustering([n_clusters, ...])
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Apply clustering to a projection of the normalized Laplacian.
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cluster.SpectralBiclustering([n_clusters, ...])
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Spectral biclustering (Kluger, 2003).
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cluster.SpectralCoclustering([n_clusters, ...])
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Spectral Co-Clustering algorithm (Dhillon, 2001).
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|cluster.AffinityPropagation(*[, damping, ...])|Perform Affinity Propagation Clustering of data.|
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|cluster.AgglomerativeClustering([...])|Agglomerative Clustering.|
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|cluster.Birch(*[, threshold, ...])|Implements the BIRCH clustering algorithm.|
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|cluster.DBSCAN([eps, min_samples, metric, ...])|Perform DBSCAN clustering from vector array or distance matrix.|
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|cluster.HDBSCAN([min_cluster_size, ...])|Cluster data using hierarchical density-based clustering.|
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|cluster.FeatureAgglomeration([n_clusters, ...])|Agglomerate features.|
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|**cluster.KMeans([n_clusters, init, n_init, ...])**|**K-Means clustering.**|
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|cluster.BisectingKMeans([n_clusters, init, ...])|Bisecting K-Means clustering.|
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|**cluster.MiniBatchKMeans([n_clusters, init, ...])**|**Mini-Batch K-Means clustering.**|
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|cluster.MeanShift(*[, bandwidth, seeds, ...])|Mean shift clustering using a flat kernel.|
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|cluster.OPTICS(*[, min_samples, max_eps, ...])|Estimate clustering structure from vector array.|
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|cluster.SpectralClustering([n_clusters, ...])|Apply clustering to a projection of the normalized Laplacian.|
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|cluster.SpectralBiclustering([n_clusters, ...])|Spectral biclustering (Kluger, 2003).|
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|cluster.SpectralCoclustering([n_clusters, ...])|Spectral Co-Clustering algorithm (Dhillon, 2001).|
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### Functions
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cluster.affinity_propagation(S, *[, ...])
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Perform Affinity Propagation Clustering of data.
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cluster.cluster_optics_dbscan(*, ...)
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Perform DBSCAN extraction for an arbitrary epsilon.
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cluster.cluster_optics_xi(*, reachability, ...)
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Automatically extract clusters according to the Xi-steep method.
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cluster.compute_optics_graph(X, *, ...)
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Compute the OPTICS reachability graph.
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cluster.dbscan(X[, eps, min_samples, ...])
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Perform DBSCAN clustering from vector array or distance matrix.
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cluster.estimate_bandwidth(X, *[, quantile, ...])
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Estimate the bandwidth to use with the mean-shift algorithm.
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cluster.k_means(X, n_clusters, *[, ...])
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Perform K-means clustering algorithm.
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cluster.kmeans_plusplus(X, n_clusters, *[, ...])
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Init n_clusters seeds according to k-means++.
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cluster.mean_shift(X, *[, bandwidth, seeds, ...])
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Perform mean shift clustering of data using a flat kernel.
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cluster.spectral_clustering(affinity, *[, ...])
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Apply clustering to a projection of the normalized Laplacian.
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cluster.ward_tree(X, *[, connectivity, ...])
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Ward clustering based on a Feature matrix.
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|cluster.affinity_propagation(S, *[, ...])|Perform Affinity Propagation Clustering of data.|
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|cluster.cluster_optics_dbscan(*, ...)|Perform DBSCAN extraction for an arbitrary epsilon.|
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|cluster.cluster_optics_xi(*, reachability, ...)|Automatically extract clusters according to the Xi-steep method.|
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|cluster.compute_optics_graph(X, *, ...)|Compute the OPTICS reachability graph.|
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|cluster.dbscan(X[, eps, min_samples, ...])|Perform DBSCAN clustering from vector array or distance matrix.|
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|cluster.estimate_bandwidth(X, *[, quantile, ...])|Estimate the bandwidth to use with the mean-shift algorithm.|
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|cluster.k_means(X, n_clusters, *[, ...])|Perform K-means clustering algorithm.|
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|cluster.kmeans_plusplus(X, n_clusters, *[, ...])|Init n_clusters seeds according to k-means++.|
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|cluster.mean_shift(X, *[, bandwidth, seeds, ...])|Perform mean shift clustering of data using a flat kernel.|
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|cluster.spectral_clustering(affinity, *[, ...])|Apply clustering to a projection of the normalized Laplacian.|
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|cluster.ward_tree(X, *[, connectivity, ...])|Ward clustering based on a Feature matrix.|
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## [sklearn.compose: Composite Estimators](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.compose)
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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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@ -139,6 +94,8 @@ 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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@ -184,6 +141,8 @@ 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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@ -205,6 +164,8 @@ see [here](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.d
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## [sklearn.decomposition: Matrix Decomposition](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.decomposition)
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decomposition.DictionaryLearning([...])
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Dictionary learning.
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@ -264,6 +225,8 @@ 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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discriminant_analysis.LinearDiscriminantAnalysis([...])
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Linear Discriminant Analysis.
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@ -273,6 +236,8 @@ Quadratic Discriminant Analysis.
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## [sklearn.dummy: Dummy estimators](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.dummy)
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dummy.DummyClassifier(*[, strategy, ...])
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DummyClassifier makes predictions that ignore the input features.
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@ -281,6 +246,8 @@ Regressor that makes predictions using simple rules.
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## [sklearn.ensemble: Ensemble Methods](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.ensemble)
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ensemble.AdaBoostClassifier([estimator, ...])
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An AdaBoost classifier.
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@ -345,6 +312,8 @@ see [here](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.e
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## [sklearn.feature_extraction: Feature Extraction](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.feature_extraction)
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feature_extraction.DictVectorizer(*[, ...])
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Transforms lists of feature-value mappings to vectors.
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### From images
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feature_extraction.image.extract_patches_2d(...)
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Reshape a 2D image into a collection of patches.
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### From text
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feature_extraction.text.CountVectorizer(*[, ...])
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Convert a collection of text documents to a matrix of token counts.
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## [sklearn.feature_selection: Feature Selection](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.feature_selection)
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feature_selection.GenericUnivariateSelect([...])
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Univariate feature selector with configurable strategy.
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@ -437,6 +412,8 @@ Estimate mutual information for a continuous target variable.
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## [sklearn.gaussian_process: Gaussian Processes]()
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gaussian_process.GaussianProcessClassifier([...])
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Gaussian process classification (GPC) based on Laplace approximation.
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@ -446,6 +423,8 @@ Gaussian process regression (GPR).
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### Kernels
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gaussian_process.kernels.CompoundKernel(kernels)
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Kernel which is composed of a set of other kernels.
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## [sklearn.impute: Impute](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.impute)
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impute.SimpleImputer(*[, missing_values, ...])
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Univariate imputer for completing missing values with simple strategies.
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## [sklearn.inspection: Inspection](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.inspection)
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inspection.partial_dependence(estimator, X, ...)
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Partial dependence of features.
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@ -512,6 +495,8 @@ Permutation importance for feature evaluation [Rd9e56ef97513-BRE].
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### Plotting
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inspection.DecisionBoundaryDisplay(*, xx0, ...)
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Decisions boundary visualization.
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@ -522,6 +507,8 @@ Partial Dependence Plot (PDP).
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## [sklearn.isotonic: Isotonic regression](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.isotonic)
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isotonic.IsotonicRegression(*[, y_min, ...])
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Isotonic regression model.
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## [sklearn.kernel_approximation: Kernel Approximation](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.kernel_approximation)
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kernel_approximation.AdditiveChi2Sampler(*)
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Approximate feature map for additive chi2 kernel.
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## [sklearn.kernel_ridge: Kernel Ridge Regression](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.kernel_ridge)
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kernel_ridge.KernelRidge([alpha, kernel, ...])
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Kernel ridge regression.
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### Linear classifiers
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linear_model.LogisticRegression([penalty, ...])
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Logistic Regression (aka logit, MaxEnt) classifier.
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### Classical linear regressors
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linear_model.LinearRegression(*[, ...])
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Ordinary least squares Linear Regression.
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### Regressors with variable selection
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linear_model.ElasticNet([alpha, l1_ratio, ...])
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Linear regression with combined L1 and L2 priors as regularizer.
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@ -633,6 +629,8 @@ Cross-validated Orthogonal Matching Pursuit model (OMP).
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### Bayesian regressors
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linear_model.ARDRegression(*[, max_iter, ...])
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Bayesian ARD regression.
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### Multi-task linear regressors with variable selection
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linear_model.MultiTaskElasticNet([alpha, ...])
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Multi-task ElasticNet model trained with L1/L2 mixed-norm as regularizer.
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### Outlier-robust regressors
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linear_model.HuberRegressor(*[, epsilon, ...])
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L2-regularized linear regression model that is robust to outliers.
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@ -670,6 +672,8 @@ Theil-Sen Estimator: robust multivariate regression model.
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### Generalized linear models (GLM) for regression
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linear_model.PoissonRegressor(*[, alpha, ...])
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Generalized Linear Model with a Poisson distribution.
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### Miscellaneous
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linear_model.PassiveAggressiveRegressor(*[, ...])
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Passive Aggressive Regressor.
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## [sklearn.manifold: Manifold Learning](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.manifold)
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manifold.Isomap(*[, n_neighbors, radius, ...])
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Isomap Embedding.
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### Model Selection Interface
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metrics.check_scoring(estimator[, scoring, ...])
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Determine scorer from user options.
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### Classification metrics
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metrics.accuracy_score(y_true, y_pred, *[, ...])
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Accuracy classification score.
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@ -841,6 +852,8 @@ Zero-one classification loss.
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### Regression metrics
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metrics.explained_variance_score(y_true, ...)
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Explained variance regression score function.
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@ -889,6 +902,8 @@ D^2 regression score function, fraction of absolute error explained.
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### Multilabel ranking metrics
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metrics.coverage_error(y_true, y_score, *[, ...])
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Coverage error measure.
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@ -901,6 +916,8 @@ Compute Ranking loss measure.
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### Clustering metrics
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metrics.adjusted_mutual_info_score(...[, ...])
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Adjusted Mutual Information between two clusterings.
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@ -951,16 +968,22 @@ V-measure cluster labeling given a ground truth.
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### Biclustering metrics
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metrics.consensus_score(a, b, *[, similarity])
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The similarity of two sets of biclusters.
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### Distance metrics
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metrics.DistanceMetric
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Uniform interface for fast distance metric functions.
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### Pairwise metrics
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metrics.pairwise.additive_chi2_kernel(X[, Y])
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Compute the additive chi-squared kernel between observations in X and Y.
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@ -1035,6 +1058,8 @@ Generate a distance matrix chunk by chunk with optional reduction.
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### Plotting
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metrics.ConfusionMatrixDisplay(...[, ...])
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Confusion Matrix visualization.
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@ -1057,6 +1082,8 @@ Calibration curve (also known as reliability diagram) visualization.
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## [sklearn.mixture: Gaussian Mixture Models](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.mixture)
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|||
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|---|---|
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mixture.BayesianGaussianMixture(*[, ...])
|
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Variational Bayesian estimation of a Gaussian mixture.
|
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|
|
@ -1068,6 +1095,8 @@ Gaussian Mixture.
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|
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### Splitter Classes
|
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|||
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||||
|---|---|
|
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model_selection.GroupKFold([n_splits])
|
||||
K-fold iterator variant with non-overlapping groups.
|
||||
|
||||
|
|
@ -1115,14 +1144,18 @@ Time Series cross-validator
|
|||
|
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### Splitter Functions
|
||||
|
||||
|||
|
||||
|---|---|
|
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model_selection.check_cv([cv, y, classifier])
|
||||
Input checker utility for building a cross-validator.
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|
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model_selection.train_test_split(*arrays[, ...])
|
||||
Split arrays or matrices into random train and test subsets.
|
||||
|
||||
##ä 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.
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue