Overview
The goal
scikit-learn is a machine learning tool kit for data analysis.
Questions to David Rotermund
pip install scikit-learn
- Simple and efficient tools for predictive data analysis
- Accessible to everybody, and reusable in various contexts
- Built on NumPy, SciPy, and matplotlib
I will keep it short and I will mark the most relevant tools in bold
sklearn.base: Base classes and utility functions
see here
sklearn.calibration: Probability 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. |
sklearn.cluster: Clustering
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). |
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. |
sklearn.compose: Composite Estimators
| 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
| 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
| 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
see here
sklearn.decomposition: Matrix Decomposition
| 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
| discriminant_analysis.LinearDiscriminantAnalysis([…]) | Linear Discriminant Analysis. |
| discriminant_analysis.QuadraticDiscriminantAnalysis(*) | Quadratic Discriminant Analysis. |
sklearn.dummy: Dummy estimators
| dummy.DummyClassifier(*[, strategy, …]) | DummyClassifier makes predictions that ignore the input features. |
| dummy.DummyRegressor(*[, strategy, …]) | Regressor that makes predictions using simple rules. |
sklearn.ensemble: Ensemble Methods
| ensemble.AdaBoostClassifier([estimator, …]) | An AdaBoost classifier. |
| ensemble.AdaBoostRegressor([estimator, …]) | An AdaBoost regressor. |
| ensemble.BaggingClassifier([estimator, …]) | A Bagging classifier. |
| ensemble.BaggingRegressor([estimator, …]) | A Bagging regressor. |
| ensemble.ExtraTreesClassifier([…]) | An extra-trees classifier. |
| ensemble.ExtraTreesRegressor([n_estimators, …]) | An extra-trees regressor. |
| ensemble.GradientBoostingClassifier(*[, …]) | Gradient Boosting for classification. |
| ensemble.GradientBoostingRegressor(*[, …]) | Gradient Boosting for regression. |
| ensemble.IsolationForest(*[, n_estimators, …]) | Isolation Forest Algorithm. |
| ensemble.RandomForestClassifier([…]) | A random forest classifier. |
| ensemble.RandomForestRegressor([…]) | A random forest regressor. |
| ensemble.RandomTreesEmbedding([…]) | An ensemble of totally random trees. |
| ensemble.StackingClassifier(estimators[, …]) | Stack of estimators with a final classifier. |
| ensemble.StackingRegressor(estimators[, …]) | Stack of estimators with a final regressor. |
| ensemble.VotingClassifier(estimators, *[, …]) | Soft Voting/Majority Rule classifier for unfitted estimators. |
| ensemble.VotingRegressor(estimators, *[, …]) | Prediction voting regressor for unfitted estimators. |
| ensemble.HistGradientBoostingRegressor([…]) | Histogram-based Gradient Boosting Regression Tree. |
| ensemble.HistGradientBoostingClassifier([…]) | Histogram-based Gradient Boosting Classification Tree. |
sklearn.exceptions: Exceptions and warnings
see here
sklearn.experimental: Experimental
see here
sklearn.feature_extraction: Feature Extraction
| feature_extraction.DictVectorizer(*[, …]) | Transforms lists of feature-value mappings to vectors. |
| feature_extraction.FeatureHasher([…]) | Implements feature hashing, aka the hashing trick. |
From images
| feature_extraction.image.extract_patches_2d(…) | Reshape a 2D image into a collection of patches. |
| feature_extraction.image.grid_to_graph(n_x, n_y) | Graph of the pixel-to-pixel connections. |
| feature_extraction.image.img_to_graph(img, *) | Graph of the pixel-to-pixel gradient connections. |
| feature_extraction.image.reconstruct_from_patches_2d(…) | Reconstruct the image from all of its patches. |
| feature_extraction.image.PatchExtractor(*[, …]) | 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. |
| feature_extraction.text.HashingVectorizer(*) | Convert a collection of text documents to a matrix of token occurrences. |
| feature_extraction.text.TfidfTransformer(*) | Transform a count matrix to a normalized tf or tf-idf representation. |
| feature_extraction.text.TfidfVectorizer(*[, …]) | Convert a collection of raw documents to a matrix of TF-IDF features. |
sklearn.feature_selection: Feature Selection
| feature_selection.GenericUnivariateSelect([…]) | Univariate feature selector with configurable strategy. |
| feature_selection.SelectPercentile([…]) | Select features according to a percentile of the highest scores. |
| feature_selection.SelectKBest([score_func, k]) | Select features according to the k highest scores. |
| feature_selection.SelectFpr([score_func, alpha]) | Filter: Select the pvalues below alpha based on a FPR test. |
| feature_selection.SelectFdr([score_func, alpha]) | Filter: Select the p-values for an estimated false discovery rate. |
| feature_selection.SelectFromModel(estimator, *) | Meta-transformer for selecting features based on importance weights. |
| feature_selection.SelectFwe([score_func, alpha]) | Filter: Select the p-values corresponding to Family-wise error rate. |
| feature_selection.SequentialFeatureSelector(…) | Transformer that performs Sequential Feature Selection. |
| feature_selection.RFE(estimator, *[, …]) | Feature ranking with recursive feature elimination. |
| feature_selection.RFECV(estimator, *[, …]) | Recursive feature elimination with cross-validation to select features. |
| feature_selection.VarianceThreshold([threshold]) | Feature selector that removes all low-variance features. |
| feature_selection.chi2(X, y) | Compute chi-squared stats between each non-negative feature and class. |
| feature_selection.f_classif(X, y) | Compute the ANOVA F-value for the provided sample. |
| feature_selection.f_regression(X, y, *[, …]) | Univariate linear regression tests returning F-statistic and p-values. |
| feature_selection.r_regression(X, y, *[, …]) | Compute Pearson’s r for each features and the target. |
| feature_selection.mutual_info_classif(X, y, *) | Estimate mutual information for a discrete target variable. |
| feature_selection.mutual_info_regression(X, y, *) | Estimate mutual information for a continuous target variable. |
sklearn.gaussian_process: Gaussian Processes
| gaussian_process.GaussianProcessClassifier([…]) | Gaussian process classification (GPC) based on Laplace approximation. |
| gaussian_process.GaussianProcessRegressor([…]) | Gaussian process regression (GPR). |
Kernels
| gaussian_process.kernels.CompoundKernel(kernels) | Kernel which is composed of a set of other kernels. |
| gaussian_process.kernels.ConstantKernel([…]) | Constant kernel. |
| gaussian_process.kernels.DotProduct([…]) | Dot-Product kernel. |
| gaussian_process.kernels.ExpSineSquared([…]) | Exp-Sine-Squared kernel (aka periodic kernel). |
| gaussian_process.kernels.Exponentiation(…) | The Exponentiation kernel takes one base kernel and a scalar parameter and combines them via |
| gaussian_process.kernels.Hyperparameter(…) | A kernel hyperparameter’s specification in form of a namedtuple. |
| gaussian_process.kernels.Kernel() | Base class for all kernels. |
| gaussian_process.kernels.Matern([…]) | Matern kernel. |
| gaussian_process.kernels.PairwiseKernel([…]) | Wrapper for kernels in sklearn.metrics.pairwise. |
| gaussian_process.kernels.Product(k1, k2) | The Product kernel takes two kernels k1 and k2 and combines them via |
| gaussian_process.kernels.RBF([length_scale, …]) | Radial basis function kernel (aka squared-exponential kernel). |
| gaussian_process.kernels.RationalQuadratic([…]) | Rational Quadratic kernel. |
| gaussian_process.kernels.Sum(k1, k2) | The Sum kernel takes two kernels k1 and k2 and combines them via |
| gaussian_process.kernels.WhiteKernel([…]) | White kernel. |
sklearn.impute: Impute
| 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
| 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). |
sklearn.isotonic: Isotonic regression
| 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
| 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
| kernel_ridge.KernelRidge([alpha, kernel, …]) | Kernel ridge regression. |
sklearn.linear_model: Linear Models
Linear classifiers
| 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. |
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). |
Bayesian regressors
| 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. |
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. |
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. |
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. |
sklearn.manifold: Manifold Learning
| 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
Model Selection Interface
| 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
| metrics.accuracy_score(y_true, y_pred, *[, …]) | Accuracy classification score. |
| metrics.auc(x, y) | Compute Area Under the Curve (AUC) using the trapezoidal rule. |
| metrics.average_precision_score(y_true, …) | Compute average precision (AP) from prediction scores. |
| metrics.balanced_accuracy_score(y_true, …) | Compute the balanced accuracy. |
| metrics.brier_score_loss(y_true, y_prob, *) | Compute the Brier score loss. |
| metrics.class_likelihood_ratios(y_true, …) | Compute binary classification positive and negative likelihood ratios. |
| metrics.classification_report(y_true, y_pred, *) | Build a text report showing the main classification metrics. |
| metrics.cohen_kappa_score(y1, y2, *[, …]) | Compute Cohen’s kappa: a statistic that measures inter-annotator agreement. |
| metrics.confusion_matrix(y_true, y_pred, *) | Compute confusion matrix to evaluate the accuracy of a classification. |
| metrics.dcg_score(y_true, y_score, *[, k, …]) | Compute Discounted Cumulative Gain. |
| metrics.det_curve(y_true, y_score[, …]) | Compute error rates for different probability thresholds. |
| metrics.f1_score(y_true, y_pred, *[, …]) | Compute the F1 score, also known as balanced F-score or F-measure. |
| metrics.fbeta_score(y_true, y_pred, *, beta) | Compute the F-beta score. |
| metrics.hamming_loss(y_true, y_pred, *[, …]) | Compute the average Hamming loss. |
| metrics.hinge_loss(y_true, pred_decision, *) | Average hinge loss (non-regularized). |
| metrics.jaccard_score(y_true, y_pred, *[, …]) | Jaccard similarity coefficient score. |
| metrics.log_loss(y_true, y_pred, *[, eps, …]) | Log loss, aka logistic loss or cross-entropy loss. |
| metrics.matthews_corrcoef(y_true, y_pred, *) | Compute the Matthews correlation coefficient (MCC). |
| metrics.multilabel_confusion_matrix(y_true, …) | Compute a confusion matrix for each class or sample. |
| metrics.ndcg_score(y_true, y_score, *[, k, …]) | Compute Normalized Discounted Cumulative Gain. |
| metrics.precision_recall_curve(y_true, …) | Compute precision-recall pairs for different probability thresholds. |
| metrics.precision_recall_fscore_support(…) | Compute precision, recall, F-measure and support for each class. |
| metrics.precision_score(y_true, y_pred, *[, …]) | Compute the precision. |
| metrics.recall_score(y_true, y_pred, *[, …]) | Compute the recall. |
| metrics.roc_auc_score(y_true, y_score, *[, …]) | Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores. |
| metrics.roc_curve(y_true, y_score, *[, …]) | Compute Receiver operating characteristic (ROC). |
| metrics.top_k_accuracy_score(y_true, y_score, *) | Top-k Accuracy classification score. |
| metrics.zero_one_loss(y_true, y_pred, *[, …]) | Zero-one classification loss. |
Regression metrics
| metrics.explained_variance_score(y_true, …) | Explained variance regression score function. |
| metrics.max_error(y_true, y_pred) | The max_error metric calculates the maximum residual error. |
| metrics.mean_absolute_error(y_true, y_pred, *) | Mean absolute error regression loss. |
| metrics.mean_squared_error(y_true, y_pred, *) | Mean squared error regression loss. |
| metrics.mean_squared_log_error(y_true, y_pred, *) | Mean squared logarithmic error regression loss. |
| metrics.median_absolute_error(y_true, y_pred, *) | Median absolute error regression loss. |
| metrics.mean_absolute_percentage_error(…) | Mean absolute percentage error (MAPE) regression loss. |
| metrics.r2_score(y_true, y_pred, *[, …]) | R^2 (coefficient of determination) regression score function. |
| metrics.mean_poisson_deviance(y_true, y_pred, *) | Mean Poisson deviance regression loss. |
| metrics.mean_gamma_deviance(y_true, y_pred, *) | Mean Gamma deviance regression loss. |
| metrics.mean_tweedie_deviance(y_true, y_pred, *) | Mean Tweedie deviance regression loss. |
| metrics.d2_tweedie_score(y_true, y_pred, *) | D^2 regression score function, fraction of Tweedie deviance explained. |
| metrics.mean_pinball_loss(y_true, y_pred, *) | Pinball loss for quantile regression. |
| metrics.d2_pinball_score(y_true, y_pred, *) | D^2 regression score function, fraction of pinball loss explained. |
| metrics.d2_absolute_error_score(y_true, …) | D^2 regression score function, fraction of absolute error explained. |
Multilabel ranking metrics
| metrics.coverage_error(y_true, y_score, *[, …]) | Coverage error measure. |
| metrics.label_ranking_average_precision_score(…) | Compute ranking-based average precision. |
| metrics.label_ranking_loss(y_true, y_score, *) | Compute Ranking loss measure. |
Clustering metrics
| metrics.adjusted_mutual_info_score(…[, …]) | Adjusted Mutual Information between two clusterings. |
| metrics.adjusted_rand_score(labels_true, …) | Rand index adjusted for chance. |
| metrics.calinski_harabasz_score(X, labels) | Compute the Calinski and Harabasz score. |
| metrics.davies_bouldin_score(X, labels) | Compute the Davies-Bouldin score. |
| metrics.completeness_score(labels_true, …) | Compute completeness metric of a cluster labeling given a ground truth. |
| metrics.cluster.contingency_matrix(…[, …]) | Build a contingency matrix describing the relationship between labels. |
| metrics.cluster.pair_confusion_matrix(…) | Pair confusion matrix arising from two clusterings [R9ca8fd06d29a-1]. |
| metrics.fowlkes_mallows_score(labels_true, …) | Measure the similarity of two clusterings of a set of points. |
| metrics.homogeneity_completeness_v_measure(…) | Compute the homogeneity and completeness and V-Measure scores at once. |
| metrics.homogeneity_score(labels_true, …) | Homogeneity metric of a cluster labeling given a ground truth. |
| metrics.mutual_info_score(labels_true, …) | Mutual Information between two clusterings. |
| metrics.normalized_mutual_info_score(…[, …]) | Normalized Mutual Information between two clusterings. |
| metrics.rand_score(labels_true, labels_pred) | Rand index. |
| metrics.silhouette_score(X, labels, *[, …]) | Compute the mean Silhouette Coefficient of all samples. |
| metrics.silhouette_samples(X, labels, *[, …]) | Compute the Silhouette Coefficient for each sample. |
| metrics.v_measure_score(labels_true, …[, beta]) | 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. |
| metrics.pairwise.chi2_kernel(X[, Y, gamma]) | Compute the exponential chi-squared kernel between X and Y. |
| metrics.pairwise.cosine_similarity(X[, Y, …]) | Compute cosine similarity between samples in X and Y. |
| metrics.pairwise.cosine_distances(X[, Y]) | Compute cosine distance between samples in X and Y. |
| metrics.pairwise.distance_metrics() | Valid metrics for pairwise_distances. |
| metrics.pairwise.euclidean_distances(X[, Y, …]) | Compute the distance matrix between each pair from a vector array X and Y. |
| metrics.pairwise.haversine_distances(X[, Y]) | Compute the Haversine distance between samples in X and Y. |
| metrics.pairwise.kernel_metrics() | Valid metrics for pairwise_kernels. |
| metrics.pairwise.laplacian_kernel(X[, Y, gamma])Compute the laplacian kernel between X and Y. | |
| metrics.pairwise.linear_kernel(X[, Y, …]) | Compute the linear kernel between X and Y. |
| metrics.pairwise.manhattan_distances(X[, Y, …]) | Compute the L1 distances between the vectors in X and Y. |
| metrics.pairwise.nan_euclidean_distances(X) | Calculate the euclidean distances in the presence of missing values. |
| metrics.pairwise.pairwise_kernels(X[, Y, …]) | Compute the kernel between arrays X and optional array Y. |
| metrics.pairwise.polynomial_kernel(X[, Y, …]) | Compute the polynomial kernel between X and Y. |
| metrics.pairwise.rbf_kernel(X[, Y, gamma]) | Compute the rbf (gaussian) kernel between X and Y. |
| metrics.pairwise.sigmoid_kernel(X[, Y, …]) | Compute the sigmoid kernel between X and Y. |
| metrics.pairwise.paired_euclidean_distances(X, Y) | Compute the paired euclidean distances between X and Y. |
| metrics.pairwise.paired_manhattan_distances(X, Y) | Compute the paired L1 distances between X and Y. |
| metrics.pairwise.paired_cosine_distances(X, Y) | Compute the paired cosine distances between X and Y. |
| metrics.pairwise.paired_distances(X, Y, *[, …]) | Compute the paired distances between X and Y. |
| metrics.pairwise_distances(X[, Y, metric, …]) | Compute the distance matrix from a vector array X and optional Y. |
| metrics.pairwise_distances_argmin(X, Y, *[, …]) | Compute minimum distances between one point and a set of points. |
| metrics.pairwise_distances_argmin_min(X, Y, *) | Compute minimum distances between one point and a set of points. |
| metrics.pairwise_distances_chunked(X[, Y, …]) | Generate a distance matrix chunk by chunk with optional reduction. |
Plotting
| metrics.ConfusionMatrixDisplay(…[, …]) | Confusion Matrix visualization. |
| metrics.DetCurveDisplay(*, fpr, fnr[, …]) | DET curve visualization. |
| metrics.PrecisionRecallDisplay(precision, …) | Precision Recall visualization. |
| metrics.PredictionErrorDisplay(*, y_true, y_pred) | Visualization of the prediction error of a regression model. |
| metrics.RocCurveDisplay(*, fpr, tpr[, …]) | ROC Curve visualization. |
| calibration.CalibrationDisplay(prob_true, …) | Calibration curve (also known as reliability diagram) visualization. |
sklearn.mixture: Gaussian Mixture Models
| mixture.BayesianGaussianMixture(*[, …]) | Variational Bayesian estimation of a Gaussian mixture. |
| mixture.GaussianMixture([n_components, …]) | Gaussian Mixture. |
sklearn.model_selection: Model Selection
Splitter Classes
| model_selection.GroupKFold([n_splits]) | K-fold iterator variant with non-overlapping groups. |
| model_selection.GroupShuffleSplit([…]) | Shuffle-Group(s)-Out cross-validation iterator |
| model_selection.KFold([n_splits, shuffle, …]) | K-Folds cross-validator |
| model_selection.LeaveOneGroupOut() | Leave One Group Out cross-validator |
| model_selection.LeavePGroupsOut(n_groups) | Leave P Group(s) Out cross-validator |
| model_selection.LeaveOneOut() | Leave-One-Out cross-validator |
| model_selection.LeavePOut(p) | Leave-P-Out cross-validator |
| model_selection.PredefinedSplit(test_fold) | Predefined split cross-validator |
| model_selection.RepeatedKFold(*[, n_splits, …]) | Repeated K-Fold cross validator. |
| model_selection.RepeatedStratifiedKFold(*[, …]) | Repeated Stratified K-Fold cross validator. |
| model_selection.ShuffleSplit([n_splits, …]) | Random permutation cross-validator |
| model_selection.StratifiedKFold([n_splits, …]) | Stratified K-Folds cross-validator. |
| model_selection.StratifiedShuffleSplit([…]) | Stratified ShuffleSplit cross-validator |
| model_selection.StratifiedGroupKFold([…]) | Stratified K-Folds iterator variant with non-overlapping groups. |
| model_selection.TimeSeriesSplit([n_splits, …]) | 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. |
Hyper-parameter optimizers
| model_selection.GridSearchCV(estimator, …) | Exhaustive search over specified parameter values for an estimator. |
| model_selection.HalvingGridSearchCV(…[, …]) | Search over specified parameter values with successive halving. |
| model_selection.ParameterGrid(param_grid) | Grid of parameters with a discrete number of values for each. |
| model_selection.ParameterSampler(…[, …]) | Generator on parameters sampled from given distributions. |
| model_selection.RandomizedSearchCV(…[, …]) | Randomized search on hyper parameters. |
| model_selection.HalvingRandomSearchCV(…[, …]) | 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. |
| model_selection.cross_val_predict(estimator, X) | Generate cross-validated estimates for each input data point. |
| model_selection.cross_val_score(estimator, X) | Evaluate a score by cross-validation. |
| model_selection.learning_curve(estimator, X, …) | Learning curve. |
| model_selection.permutation_test_score(…) | Evaluate the significance of a cross-validated score with permutations. |
| model_selection.validation_curve(estimator, …) | Validation curve. |
Visualization
| model_selection.LearningCurveDisplay(*, …) | Learning Curve visualization. |
| model_selection.ValidationCurveDisplay(*, …) | Validation Curve visualization. |
sklearn.multiclass: Multiclass classification
| multiclass.OneVsRestClassifier(estimator, *) | One-vs-the-rest (OvR) multiclass strategy. |
| multiclass.OneVsOneClassifier(estimator, *) | One-vs-one multiclass strategy. |
| multiclass.OutputCodeClassifier(estimator, *) | (Error-Correcting) Output-Code multiclass strategy. |
sklearn.multioutput: Multioutput regression and classification
| multioutput.ClassifierChain(base_estimator, *) | A multi-label model that arranges binary classifiers into a chain. |
| multioutput.MultiOutputRegressor(estimator, *) | Multi target regression. |
| multioutput.MultiOutputClassifier(estimator, *) | Multi target classification. |
| multioutput.RegressorChain(base_estimator, *) | A multi-label model that arranges regressions into a chain. |
sklearn.naive_bayes: Naive Bayes
| naive_bayes.BernoulliNB(*[, alpha, …]) | Naive Bayes classifier for multivariate Bernoulli models. |
| naive_bayes.CategoricalNB(*[, alpha, …]) | Naive Bayes classifier for categorical features. |
| naive_bayes.ComplementNB(*[, alpha, …]) | The Complement Naive Bayes classifier described in Rennie et al. (2003). |
| naive_bayes.GaussianNB(*[, priors, …]) | Gaussian Naive Bayes (GaussianNB). |
| naive_bayes.MultinomialNB(*[, alpha, …]) | Naive Bayes classifier for multinomial models. |
sklearn.neighbors: Nearest Neighbors
| neighbors.BallTree(X[, leaf_size, metric]) | BallTree for fast generalized N-point problems |
| neighbors.KDTree(X[, leaf_size, metric]) | KDTree for fast generalized N-point problems |
| neighbors.KernelDensity(*[, bandwidth, …]) | Kernel Density Estimation. |
| neighbors.KNeighborsClassifier([…]) | Classifier implementing the k-nearest neighbors vote. |
| neighbors.KNeighborsRegressor([n_neighbors, …]) | Regression based on k-nearest neighbors. |
| neighbors.KNeighborsTransformer(*[, mode, …]) | Transform X into a (weighted) graph of k nearest neighbors. |
| neighbors.LocalOutlierFactor([n_neighbors, …]) | Unsupervised Outlier Detection using the Local Outlier Factor (LOF). |
| neighbors.RadiusNeighborsClassifier([…]) | Classifier implementing a vote among neighbors within a given radius. |
| neighbors.RadiusNeighborsRegressor([radius, …]) | Regression based on neighbors within a fixed radius. |
| neighbors.RadiusNeighborsTransformer(*[, …]) | Transform X into a (weighted) graph of neighbors nearer than a radius. |
| neighbors.NearestCentroid([metric, …]) | Nearest centroid classifier. |
| neighbors.NearestNeighbors(*[, n_neighbors, …]) | Unsupervised learner for implementing neighbor searches. |
| neighbors.NeighborhoodComponentsAnalysis([…]) | Neighborhood Components Analysis. |
| neighbors.kneighbors_graph(X, n_neighbors, *) | Compute the (weighted) graph of k-Neighbors for points in X. |
| neighbors.radius_neighbors_graph(X, radius, *) | Compute the (weighted) graph of Neighbors for points in X. |
| neighbors.sort_graph_by_row_values(graph[, …]) | Sort a sparse graph such that each row is stored with increasing values. |
sklearn.neural_network: Neural network models
| pipeline.FeatureUnion(transformer_list, *[, …]) | Concatenates results of multiple transformer objects. |
| pipeline.Pipeline(steps, *[, memory, verbose]) | Pipeline of transforms with a final estimator. |
| pipeline.make_pipeline(*steps[, memory, verbose]) | Construct a Pipeline from the given estimators. |
| pipeline.make_union(*transformers[, n_jobs, …]) | Construct a FeatureUnion from the given transformers. |
sklearn.pipeline: Pipeline
see here
sklearn.preprocessing: Preprocessing and Normalization
| preprocessing.Binarizer(*[, threshold, copy]) | Binarize data (set feature values to 0 or 1) according to a threshold. |
| preprocessing.FunctionTransformer([func, …]) | Constructs a transformer from an arbitrary callable. |
| preprocessing.KBinsDiscretizer([n_bins, …]) | Bin continuous data into intervals. |
| preprocessing.KernelCenterer() | Center an arbitrary kernel matrix |
| preprocessing.LabelBinarizer(*[, neg_label, …]) | Binarize labels in a one-vs-all fashion. |
| preprocessing.LabelEncoder() | Encode target labels with value between 0 and n_classes-1.v |
| preprocessing.MultiLabelBinarizer(*[, …]) | Transform between iterable of iterables and a multilabel format. |
| preprocessing.MaxAbsScaler(*[, copy]) | Scale each feature by its maximum absolute value. |
| preprocessing.MinMaxScaler([feature_range, …]) | Transform features by scaling each feature to a given range. |
| preprocessing.Normalizer([norm, copy]) | Normalize samples individually to unit norm. |
| preprocessing.OneHotEncoder(*[, categories, …]) | Encode categorical features as a one-hot numeric array. |
| preprocessing.OrdinalEncoder(*[, …]) | Encode categorical features as an integer array. |
| preprocessing.PolynomialFeatures([degree, …]) | Generate polynomial and interaction features. |
| preprocessing.PowerTransformer([method, …]) | Apply a power transform featurewise to make data more Gaussian-like. |
| preprocessing.QuantileTransformer(*[, …]) | Transform features using quantiles information. |
| preprocessing.RobustScaler(*[, …]) | Scale features using statistics that are robust to outliers. |
| preprocessing.SplineTransformer([n_knots, …]) | Generate univariate B-spline bases for features. |
| preprocessing.StandardScaler(*[, copy, …]) | Standardize features by removing the mean and scaling to unit variance. |
| preprocessing.TargetEncoder([categories, …]) | Target Encoder for regression and classification targets. |
| preprocessing.add_dummy_feature(X[, value]) | Augment dataset with an additional dummy feature. |
| preprocessing.binarize(X, *[, threshold, copy]) | Boolean thresholding of array-like or scipy.sparse matrix. |
| preprocessing.label_binarize(y, *, classes) | Binarize labels in a one-vs-all fashion. |
| preprocessing.maxabs_scale(X, *[, axis, copy]) | Scale each feature to the [-1, 1] range without breaking the sparsity. |
| preprocessing.minmax_scale(X[, …]) | Transform features by scaling each feature to a given range. |
| preprocessing.normalize(X[, norm, axis, …]) | Scale input vectors individually to unit norm (vector length). |
| preprocessing.quantile_transform(X, *[, …]) | Transform features using quantiles information. |
| preprocessing.robust_scale(X, *[, axis, …]) | Standardize a dataset along any axis. |
| preprocessing.scale(X, *[, axis, with_mean, …]) | Standardize a dataset along any axis. |
| preprocessing.power_transform(X[, method, …]) | Parametric, monotonic transformation to make data more Gaussian-like. |
sklearn.random_projection: Random projection
| random_projection.GaussianRandomProjection([…]) | Reduce dimensionality through Gaussian random projection. |
| random_projection.SparseRandomProjection([…]) | Reduce dimensionality through sparse random projection. |
| random_projection.johnson_lindenstrauss_min_dim(…) | Find a ‘safe’ number of components to randomly project to. |
sklearn.semi_supervised: Semi-Supervised Learning
| semi_supervised.LabelPropagation([kernel, …]) | Label Propagation classifier. |
| semi_supervised.LabelSpreading([kernel, …]) | LabelSpreading model for semi-supervised learning. |
| semi_supervised.SelfTrainingClassifier(…) | Self-training classifier. |
sklearn.svm: Support Vector Machines
| svm.LinearSVC([penalty, loss, dual, tol, C, …]) | Linear Support Vector Classification. |
| svm.LinearSVR(*[, epsilon, tol, C, loss, …]) | Linear Support Vector Regression. |
| svm.NuSVC(*[, nu, kernel, degree, gamma, …]) | Nu-Support Vector Classification. |
| svm.NuSVR(*[, nu, C, kernel, degree, gamma, …]) | Nu Support Vector Regression. |
| svm.OneClassSVM(*[, kernel, degree, gamma, …]) | Unsupervised Outlier Detection. |
| svm.SVC(*[, C, kernel, degree, gamma, …]) | C-Support Vector Classification. |
| svm.SVR(*[, kernel, degree, gamma, coef0, …]) | Epsilon-Support Vector Regression. |
| svm.l1_min_c(X, y, *[, loss, fit_intercept, …]) | Return the lowest bound for C. |
sklearn.tree: Decision Trees
| tree.DecisionTreeClassifier(*[, criterion, …]) | A decision tree classifier. |
| tree.DecisionTreeRegressor(*[, criterion, …]) | A decision tree regressor. |
| tree.ExtraTreeClassifier(*[, criterion, …]) | An extremely randomized tree classifier. |
| tree.ExtraTreeRegressor(*[, criterion, …]) | An extremely randomized tree regressor. |
| tree.export_graphviz(decision_tree[, …]) | Export a decision tree in DOT format. |
| tree.export_text(decision_tree, *[, …]) | Build a text report showing the rules of a decision tree. |
| tree.plot_tree(decision_tree, *[, …]) | Plot a decision tree. |
sklearn.utils: Utilities
see here
The source code is Open Source and can be found on GitHub.