8. Reference¶
This is the class and function reference of scikit-learn. Please refer to the full user guide for further details, as the class and function raw specifications may not be enough to give full guidelines on their uses.
List of modules
- sklearn.cluster: Clustering
- sklearn.covariance: Covariance Estimators
- sklearn.cross_validation: Cross Validation
- sklearn.datasets: Datasets
- sklearn.decomposition: Matrix Decomposition
- sklearn.ensemble: Ensemble Methods
- sklearn.feature_extraction: Feature Extraction
- sklearn.feature_selection: Feature Selection
- sklearn.gaussian_process: Gaussian Processes
- sklearn.grid_search: Grid Search
- sklearn.hmm: Hidden Markov Models
- sklearn.kernel_approximation Kernel Approximation
- sklearn.lda: Linear Discriminant Analysis
- sklearn.linear_model: Generalized Linear Models
- sklearn.manifold: Manifold Learning
- sklearn.metrics: Metrics
- sklearn.mixture: Gaussian Mixture Models
- sklearn.multiclass: Multiclass and multilabel classification
- sklearn.naive_bayes: Naive Bayes
- sklearn.neighbors: Nearest Neighbors
- sklearn.pls: Partial Least Squares
- sklearn.pipeline: Pipeline
- sklearn.preprocessing: Preprocessing and Normalization
- sklearn.svm: Support Vector Machines
- sklearn.tree: Decision Trees
- sklearn.utils: Utilities
8.1. sklearn.cluster: Clustering¶
The sklearn.cluster module gathers popular unsupervised clustering algorithms.
User guide: See the Clustering section for further details.
| cluster.AffinityPropagation([damping, ...]) | Perform Affinity Propagation Clustering of data |
| cluster.DBSCAN([eps, min_samples, metric, ...]) | Perform DBSCAN clustering from vector array or distance matrix. |
| cluster.KMeans([k, init, n_init, max_iter, ...]) | K-Means clustering |
| cluster.MiniBatchKMeans([k, init, max_iter, ...]) | Mini-Batch K-Means clustering |
| cluster.MeanShift([bandwidth, seeds, ...]) | MeanShift clustering |
| cluster.SpectralClustering([k, mode, ...]) | Apply k-means to a projection to the normalized laplacian |
| cluster.Ward([n_clusters, memory, ...]) | Ward hierarchical clustering: constructs a tree and cuts it. |
8.2. sklearn.covariance: Covariance Estimators¶
The sklearn.covariance module includes methods and algorithms to robustly estimate the covariance of features given a set of points. The precision matrix defined as the inverse of the covariance is also estimated. Covariance estimation is closely related to the theory of Gaussian Graphical Models.
User guide: See the Covariance estimation section for further details.
| covariance.EmpiricalCovariance([...]) | Maximum likelihood covariance estimator |
| covariance.ShrunkCovariance([...]) | Covariance estimator with shrinkage |
| covariance.LedoitWolf([store_precision, ...]) | LedoitWolf Estimator |
| covariance.OAS([store_precision, ...]) | Oracle Approximating Shrinkage Estimator |
| covariance.GraphLasso([alpha, mode, tol, ...]) | Sparse inverse covariance estimation with an l1-penalized estimator. |
| covariance.GraphLassoCV([alphas, ...]) | Sparse inverse covariance w/ cross-validated choice of the l1 penality |
| covariance.MinCovDet([store_precision, ...]) | Minimum Covariance Determinant (MCD): robust estimator of covariance |
| covariance.empirical_covariance(X[, ...]) | Computes the Maximum likelihood covariance estimator |
| covariance.ledoit_wolf(X[, assume_centered]) | Estimates the shrunk Ledoit-Wolf covariance matrix. |
| covariance.shrunk_covariance(emp_cov[, ...]) | Calculates a covariance matrix shrunk on the diagonal |
| covariance.oas(X[, assume_centered]) | Estimate covariance with the Oracle Approximating Shrinkage algorithm. |
| covariance.graph_lasso(emp_cov, alpha[, ...]) | l1-penalized covariance estimator |
8.3. sklearn.cross_validation: Cross Validation¶
The sklearn.cross_validation module includes utilities for cross- validation and performance evaluation.
User guide: See the Cross-Validation section for further details.
| cross_validation.LeaveOneOut(n[, indices]) | Leave-One-Out cross validation iterator. |
| cross_validation.LeavePOut(n, p[, indices]) | Leave-P-Out cross validation iterator |
| cross_validation.KFold(n, k[, indices]) | K-Folds cross validation iterator |
| cross_validation.StratifiedKFold(y, k[, indices]) | Stratified K-Folds cross validation iterator |
| cross_validation.LeaveOneLabelOut(labels[, ...]) | Leave-One-Label_Out cross-validation iterator |
| cross_validation.LeavePLabelOut(labels, p[, ...]) | Leave-P-Label_Out cross-validation iterator |
| cross_validation.Bootstrap(n[, ...]) | Random sampling with replacement cross-validation iterator |
| cross_validation.ShuffleSplit(n[, ...]) | Random permutation cross-validation iterator. |
8.4. sklearn.datasets: Datasets¶
The sklearn.datasets module includes utilities to load datasets, including methods to load and fetch popular reference datasets. It also features some artificial data generators.
User guide: See the Dataset loading utilities section for further details.
8.4.1. Loaders¶
| datasets.load_20newsgroups(*args, **kwargs) | DEPRECATED: Use fetch_20newsgroups instead with download_if_missing=False |
| datasets.fetch_20newsgroups([data_home, ...]) | Load the filenames of the 20 newsgroups dataset. |
| datasets.fetch_20newsgroups_vectorized([...]) | Load the 20 newsgroups dataset and transform it into tf-idf vectors. |
| datasets.load_boston() | Load and return the boston house-prices dataset (regression). |
| datasets.load_diabetes() | Load and return the diabetes dataset (regression). |
| datasets.load_digits([n_class]) | Load and return the digits dataset (classification). |
| datasets.load_files(container_path[, ...]) | Load text files with categories as subfolder names. |
| datasets.load_iris() | Load and return the iris dataset (classification). |
| datasets.load_lfw_pairs([download_if_missing]) | Alias for fetch_lfw_pairs(download_if_missing=False) |
| datasets.fetch_lfw_pairs([subset, ...]) | Loader for the Labeled Faces in the Wild (LFW) pairs dataset |
| datasets.load_lfw_people([download_if_missing]) | Alias for fetch_lfw_people(download_if_missing=False) |
| datasets.fetch_lfw_people([data_home, ...]) | Loader for the Labeled Faces in the Wild (LFW) people dataset |
| datasets.load_linnerud() | Load and return the linnerud dataset (multivariate regression). |
| datasets.fetch_olivetti_faces([data_home, ...]) | Loader for the Olivetti faces data-set from AT&T. |
| datasets.load_sample_image(image_name) | Load the numpy array of a single sample image |
| datasets.load_sample_images() | Load sample images for image manipulation. |
8.4.2. Samples generator¶
| datasets.make_blobs([n_samples, n_features, ...]) | Generate isotropic Gaussian blobs for clustering. |
| datasets.make_classification([n_samples, ...]) | Generate a random n-class classification problem. |
| datasets.make_friedman1([n_samples, ...]) | Generate the “Friedman #1” regression problem |
| datasets.make_friedman2([n_samples, noise, ...]) | Generate the “Friedman #2” regression problem |
| datasets.make_friedman3([n_samples, noise, ...]) | Generate the “Friedman #3” regression problem |
| datasets.make_low_rank_matrix([n_samples, ...]) | Generate a mostly low rank matrix with bell-shaped singular values |
| datasets.make_multilabel_classification([...]) | Generate a random multilabel classification problem. |
| datasets.make_regression([n_samples, ...]) | Generate a random regression problem. |
| datasets.make_s_curve([n_samples, noise, ...]) | Generate an S curve dataset. |
| datasets.make_sparse_coded_signal(n_samples, ...) | Generate a signal as a sparse combination of dictionary elements. |
| datasets.make_sparse_spd_matrix([dim, ...]) | Generate a sparse symetric definite positive matrix. |
| datasets.make_sparse_uncorrelated([...]) | Generate a random regression problem with sparse uncorrelated design |
| datasets.make_spd_matrix(n_dim[, random_state]) | Generate a random symmetric, positive-definite matrix. |
| datasets.make_swiss_roll([n_samples, noise, ...]) | Generate a swiss roll dataset. |
8.5. sklearn.decomposition: Matrix Decomposition¶
The sklearn.decomposition module includes matrix decomposition algorithms, including among others PCA, NMF or ICA. Most of the algorithms of this module can be regarded as dimensionality reduction techniques.
User guide: See the Decomposing signals in components (matrix factorization problems) section for further details.
| decomposition.PCA([n_components, copy, whiten]) | Principal component analysis (PCA) |
| decomposition.ProbabilisticPCA([...]) | Additional layer on top of PCA that adds a probabilistic evaluationPrincipal component analysis (PCA) |
| decomposition.ProjectedGradientNMF([...]) | Non-Negative matrix factorization by Projected Gradient (NMF) |
| decomposition.RandomizedPCA(n_components[, ...]) | Principal component analysis (PCA) using randomized SVD |
| decomposition.KernelPCA([n_components, ...]) | Kernel Principal component analysis (KPCA) |
| decomposition.FastICA([n_components, ...]) | FastICA; a fast algorithm for Independent Component Analysis |
| decomposition.NMF([n_components, init, ...]) | Non-Negative matrix factorization by Projected Gradient (NMF) |
| decomposition.SparsePCA(n_components[, ...]) | Sparse Principal Components Analysis (SparsePCA) |
| decomposition.MiniBatchSparsePCA(n_components) | Mini-batch Sparse Principal Components Analysis |
| decomposition.SparseCoder(dictionary[, ...]) | Sparse coding |
| decomposition.DictionaryLearning(n_atoms[, ...]) | Dictionary learning |
| decomposition.MiniBatchDictionaryLearning(n_atoms) | Mini-batch dictionary learning |
| decomposition.fastica(X[, n_components, ...]) | Perform Fast Independent Component Analysis. |
| decomposition.dict_learning(X, n_atoms, alpha) | Solves a dictionary learning matrix factorization problem. |
| decomposition.dict_learning_online(X, ...[, ...]) | Solves a dictionary learning matrix factorization problem online. |
| decomposition.sparse_encode(X, dictionary[, ...]) | Sparse coding |
8.6. sklearn.ensemble: Ensemble Methods¶
The sklearn.ensemble module includes ensemble-based methods for classification and regression.
User guide: See the Ensemble methods section for further details.
| ensemble.RandomForestClassifier([...]) | A random forest classifier. |
| ensemble.RandomForestRegressor([...]) | A random forest regressor. |
| ensemble.ExtraTreesClassifier([...]) | An extra-trees classifier. |
| ensemble.ExtraTreesRegressor([n_estimators, ...]) | An extra-trees regressor. |
8.7. sklearn.feature_extraction: Feature Extraction¶
The sklearn.feature_extraction module deals with feature extraction from raw data. It currently includes methods to extract features from text and images.
User guide: See the Feature extraction section for further details.
8.7.1. From images¶
The sklearn.feature_extraction.image submodule gathers utilities to extract features from images.
| feature_extraction.image.img_to_graph(img[, ...]) | Graph of the pixel-to-pixel gradient connections |
| feature_extraction.image.grid_to_graph(n_x, n_y) | Graph of the pixel-to-pixel connections |
| feature_extraction.image.extract_patches_2d(...) | Reshape a 2D image into a collection of patches |
| 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 |
8.7.2. From text¶
The sklearn.feature_extraction.text submodule gathers utilities to build feature vectors from text documents.
| feature_extraction.text.RomanPreprocessor | Fast preprocessor suitable for Latin alphabet text |
| feature_extraction.text.WordNGramAnalyzer([...]) | Simple analyzer: transform text document into a sequence of word tokens |
| feature_extraction.text.CharNGramAnalyzer([...]) | Compute character n-grams features of a text document |
| feature_extraction.text.CountVectorizer(...) | Convert a collection of raw documents to a matrix of token counts |
| feature_extraction.text.TfidfTransformer([...]) | Transform a count matrix to a normalized tf or tf–idf representation |
| feature_extraction.text.Vectorizer(...[, ...]) | Convert a collection of raw documents to a matrix |
8.8. sklearn.feature_selection: Feature Selection¶
The sklearn.feature_selection module implements feature selection algorithms. It currently includes univariate filter selection methods and the recursive feature elimination algorithm.
User guide: See the Feature selection section for further details.
| feature_selection.SelectPercentile(score_func) | Filter: Select the best percentile of the p_values |
| feature_selection.SelectKBest(score_func[, k]) | Filter: Select the k lowest p-values |
| 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.SelectFwe(score_func[, alpha]) | Filter: Select the p-values corresponding to Family-wise error rate |
| feature_selection.RFE(estimator, ...[, step]) | Feature ranking with recursive feature elimination. |
| feature_selection.RFECV(estimator[, step, ...]) | Feature ranking with recursive feature elimination and cross-validated selection of the best number of features. |
| feature_selection.chi2(X, y) | Compute χ² (chi-squared) statistic for each class/feature combination. |
| feature_selection.f_classif(X, y) | Compute the Anova F-value for the provided sample |
| feature_selection.f_regression(X, y[, center]) | Univariate linear regression tests |
8.9. sklearn.gaussian_process: Gaussian Processes¶
The sklearn.gaussian_process module implements scalar Gaussian Process based predictions.
User guide: See the Gaussian Processes section for further details.
| gaussian_process.GaussianProcess([regr, ...]) | The Gaussian Process model class. |
| gaussian_process.correlation_models.absolute_exponential(...) | Absolute exponential autocorrelation model. |
| gaussian_process.correlation_models.squared_exponential(...) | Squared exponential correlation model (Radial Basis Function). |
| gaussian_process.correlation_models.generalized_exponential(...) | Generalized exponential correlation model. |
| gaussian_process.correlation_models.pure_nugget(...) | Spatial independence correlation model (pure nugget). |
| gaussian_process.correlation_models.cubic(...) | Cubic correlation model: |
| gaussian_process.correlation_models.linear(...) | Linear correlation model: |
| gaussian_process.regression_models.constant(x) | Zero order polynomial (constant, p = 1) regression model. |
| gaussian_process.regression_models.linear(x) | First order polynomial (linear, p = n+1) regression model. |
| gaussian_process.regression_models.quadratic(x) | Second order polynomial (quadratic, p = n*(n-1)/2+n+1) regression model. |
8.10. sklearn.grid_search: Grid Search¶
The sklearn.grid_search includes utilities to fine-tune the parameters of an estimator.
User guide: See the Grid Search section for further details.
| grid_search.GridSearchCV(estimator, param_grid) | Grid search on the parameters of a classifier |
| grid_search.IterGrid(param_grid) | Generators on the combination of the various parameter lists given |
8.11. sklearn.hmm: Hidden Markov Models¶
The sklearn.hmm module implements hidden Markov models.
Warning: sklearn.hmm is orphaned, undocumented and has known numerical stability issues. If nobody volunteers to write documentation and make it more stable, this module will be removed in version 0.11.
User guide: See the Hidden Markov Models section for further details.
| hmm.GaussianHMM([n_components, cvtype, ...]) | Hidden Markov Model with Gaussian emissions |
| hmm.MultinomialHMM([n_components, ...]) | Hidden Markov Model with multinomial (discrete) emissions |
| hmm.GMMHMM([n_components, n_mix, startprob, ...]) | Hidden Markov Model with Gaussin mixture emissions |
8.12. sklearn.kernel_approximation Kernel Approximation¶
The sklearn.kernel_approximation module implements several approximate kernel feature maps base on Fourier transforms.
User guide: See the Kernel Approximation section for further details.
| kernel_approximation.RBFSampler([gamma, ...]) | Approximates feature map of an RBF kernel by Monte Carlo approximation |
| kernel_approximation.AdditiveChi2Sampler([...]) | Approximate feature map for additive chi² kernel. |
| kernel_approximation.SkewedChi2Sampler([...]) | Approximates feature map of the “skewed chi-squared” kernel by Monte |
8.13. sklearn.lda: Linear Discriminant Analysis¶
The sklearn.lda module implements Linear Discriminant Analysis (LDA).
| lda.LDA([n_components, priors]) | Linear Discriminant Analysis (LDA) |
8.14. sklearn.linear_model: Generalized Linear Models¶
The sklearn.linear_model module implements genelarized linear models. It includes Ridge regression, Bayesian Regression, Lasso and Elastic Net estimators computed with Least Angle Regression and coordinate descent. It also implements Stochastic Gradient Descent related algorithms.
User guide: See the Generalized Linear Models section for further details.
8.14.1. For dense data¶
| 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.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.ElasticNet([alpha, rho, ...]) | Linear Model trained with L1 and L2 prior as regularizer |
| linear_model.ElasticNetCV([rho, eps, ...]) | Elastic Net model with iterative fitting along a regularization path |
| linear_model.Lars([fit_intercept, verbose, ...]) | Least Angle Regression model a.k.a. LAR |
| linear_model.LassoLars([alpha, ...]) | Lasso model fit with Least Angle Regression a.k.a. Lars |
| linear_model.LarsCV([fit_intercept, ...]) | Cross-validated Least Angle Regression model |
| 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.LogisticRegression([penalty, ...]) | Logistic Regression classifier. |
| linear_model.OrthogonalMatchingPursuit([...]) | Orthogonal Mathching Pursuit model (OMP) |
| linear_model.SGDClassifier([loss, penalty, ...]) | Linear model fitted by minimizing a regularized empirical loss with SGD. |
| linear_model.SGDRegressor([loss, penalty, ...]) | Linear model fitted by minimizing a regularized empirical loss with SGD |
| linear_model.BayesianRidge([n_iter, tol, ...]) | Bayesian ridge regression |
| linear_model.ARDRegression([n_iter, tol, ...]) | Bayesian ARD regression. |
| linear_model.lasso_path(X, y[, eps, ...]) | Compute Lasso path with coordinate descent |
| linear_model.lars_path(X, y[, Xy, Gram, ...]) | Compute Least Angle Regression and Lasso path |
| linear_model.orthogonal_mp(X, y[, ...]) | Orthogonal Matching Pursuit (OMP) |
| linear_model.orthogonal_mp_gram(Gram, Xy[, ...]) | Gram Orthogonal Matching Pursuit (OMP) |
8.14.2. For sparse data¶
The sklearn.linear_model.sparse submodule is the sparse counterpart of the sklearn.linear_model module.
User guide: See the Generalized Linear Models section for further details.
| linear_model.sparse.Lasso([alpha, ...]) | Linear Model trained with L1 prior as regularizer |
| linear_model.sparse.ElasticNet([alpha, rho, ...]) | Linear Model trained with L1 and L2 prior as regularizer |
| linear_model.sparse.SGDClassifier([loss, ...]) | Linear model fitted by minimizing a regularized empirical loss with SGD |
| linear_model.sparse.SGDRegressor([loss, ...]) | Linear model fitted by minimizing a regularized empirical loss with SGD |
| linear_model.sparse.LogisticRegression([...]) | Logistic Regression. |
8.15. sklearn.manifold: Manifold Learning¶
The sklearn.manifold module implements data embedding techniques.
User guide: See the Manifold learning section for further details.
| manifold.LocallyLinearEmbedding([...]) | Locally Linear Embedding |
| manifold.Isomap([n_neighbors, out_dim, ...]) | Isomap Embedding |
| manifold.locally_linear_embedding(X, ...[, ...]) | Perform a Locally Linear Embedding analysis on the data. |
8.16. sklearn.metrics: Metrics¶
The sklearn.metrics module includes score functions, performance metrics and pairwise metrics and distance computations.
8.16.1. Classification metrics¶
| metrics.confusion_matrix(y_true, y_pred[, ...]) | Compute confusion matrix to evaluate the accuracy of a classification |
| metrics.roc_curve(y_true, y_score) | compute Receiver operating characteristic (ROC) |
| metrics.auc(x, y) | Compute Area Under the Curve (AUC) using the trapezoidal rule |
| metrics.precision_score(y_true, y_pred[, ...]) | Compute the precision |
| metrics.recall_score(y_true, y_pred[, pos_label]) | Compute the recall |
| metrics.fbeta_score(y_true, y_pred, beta[, ...]) | Compute fbeta score |
| metrics.f1_score(y_true, y_pred[, pos_label]) | Compute f1 score |
| metrics.precision_recall_fscore_support(...) | Compute precisions, recalls, f-measures and support for each class |
| metrics.classification_report(y_true, y_pred) | Build a text report showing the main classification metrics |
| metrics.precision_recall_curve(y_true, ...) | Compute precision-recall pairs for different probability thresholds |
| metrics.zero_one_score(y_true, y_pred) | Zero-One classification score |
| metrics.zero_one(y_true, y_pred) | Zero-One classification loss |
| metrics.hinge_loss(y_true, pred_decision[, ...]) | Cumulated hinge loss (non-regularized). |
8.16.2. Regression metrics¶
| metrics.r2_score(y_true, y_pred) | R^2 (coefficient of determination) regression score function |
| metrics.mean_square_error(y_true, y_pred) | Mean square error regression loss |
8.16.3. Clustering metrics¶
See the Clustering section of the user guide for further details.
The sklearn.metrics.cluster submodule contains evaluation metrics for cluster analysis results. There are two forms of evaluation:
- supervised, which uses a ground truth class values for each sample.
- unsupervised, which does not and measures the ‘quality’ of the model itself.
| metrics.adjusted_rand_score(labels_true, ...) | Rand index adjusted for chance |
| metrics.adjusted_mutual_info_score(...) | Adjusted Mutual Information between two clusterings |
| 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.completeness_score(labels_true, ...) | Completeness metric of a cluster labeling given a ground truth |
| metrics.v_measure_score(labels_true, labels_pred) | V-Measure cluster labeling given a ground truth |
| metrics.silhouette_score(X, labels[, ...]) | Compute the mean Silhouette Coefficient of all samples. |
8.16.4. Pairwise metrics¶
The sklearn.metrics.pairwise submodule implements utilities to evaluate pairwise distances or affinity of sets of samples.
This module contains both distance metrics and kernels. A brief summary is given on the two here.
Distance metrics are a function d(a, b) such that d(a, b) < d(a, c) if objects a and b are considered “more similar” to objects a and c. Two objects exactly alike would have a distance of zero. One of the most popular examples is Euclidean distance. To be a ‘true’ metric, it must obey the following four conditions:
1. d(a, b) >= 0, for all a and b
2. d(a, b) == 0, if and only if a = b, positive definiteness
3. d(a, b) == d(b, a), symmetry
4. d(a, c) <= d(a, b) + d(b, c), the triangle inequality
Kernels are measures of similarity, i.e. s(a, b) > s(a, c) if objects a and b are considered “more similar” to objects a and c. A kernel must also be positive semi-definite.
There are a number of ways to convert between a distance metric and a similarity measure, such as a kernel. Let D be the distance, and S be the kernel:
1. ``S = np.exp(-D * gamma)``, where one heuristic for choosing
``gamma`` is ``1 / num_features``
2. ``S = 1. / (D / np.max(D))``
| metrics.pairwise.euclidean_distances(X[, Y, ...]) | Considering the rows of X (and Y=X) as vectors, compute the |
| metrics.pairwise.manhattan_distances(X[, Y, ...]) | Compute the L1 distances between the vectors in X and Y. |
| metrics.pairwise.linear_kernel(X[, Y]) | Compute the linear kernel between X and 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.distance_metrics() | Valid metrics for pairwise_distances |
| metrics.pairwise.pairwise_distances(X[, Y, ...]) | Compute the distance matrix from a vector array X and optional Y. |
| metrics.pairwise.kernel_metrics() | Valid metrics for pairwise_kernels |
| metrics.pairwise.pairwise_kernels(X[, Y, metric]) | Compute the kernel between arrays X and optional array Y. |
8.17. sklearn.mixture: Gaussian Mixture Models¶
The sklearn.mixture module implements mixture modeling algorithms.
User guide: See the Gaussian mixture models section for further details.
| mixture.GMM([n_components, cvtype, ...]) | Gaussian Mixture Model |
| mixture.DPGMM([n_components, cvtype, alpha, ...]) | Variational Inference for the Infinite Gaussian Mixture Model. |
| mixture.VBGMM([n_components, cvtype, alpha, ...]) | Variational Inference for the Gaussian Mixture Model |
8.18. sklearn.multiclass: Multiclass and multilabel classification¶
8.18.1. Multiclass and multilabel classification strategies¶
- This module implements multiclass learning algorithms:
- one-vs-the-rest / one-vs-all
- one-vs-one
- error correcting output codes
The estimators provided in this module are meta-estimators: they require a base estimator to be provided in their constructor. For example, it is possible to use these estimators to turn a binary classifier or a regressor into a multiclass classifier. It is also possible to use these estimators with multiclass estimators in the hope that their accuracy or runtime performance improves.
User guide: See the Multiclass and multilabel algorithms section for further details.
| multiclass.OneVsRestClassifier(estimator) | One-vs-the-rest (OvR) multiclass/multilabel strategy |
| multiclass.OneVsOneClassifier(estimator) | One-vs-one multiclass strategy |
| multiclass.OutputCodeClassifier(estimator[, ...]) | (Error-Correcting) Output-Code multiclass strategy |
| multiclass.fit_ovr(estimator, X, y) | Fit a one-vs-the-rest strategy. |
| multiclass.predict_ovr(estimators, ...) | Make predictions using the one-vs-the-rest strategy. |
| multiclass.fit_ovo(estimator, X, y) | Fit a one-vs-one strategy. |
| multiclass.predict_ovo(estimators, classes, X) | Make predictions using the one-vs-one strategy. |
| multiclass.fit_ecoc(estimator, X, y[, ...]) | Fit an error-correcting output-code strategy. |
| multiclass.predict_ecoc(estimators, classes, ...) | Make predictions using the error-correcting output-code strategy. |
8.19. sklearn.naive_bayes: Naive Bayes¶
The sklearn.naive_bayes module implements Naive Bayes algorithms. These are supervised learning methods based on applying Bayes’ theorem with strong (naive) feature independence assumptions.
User guide: See the Naive Bayes section for further details.
| naive_bayes.GaussianNB | Gaussian Naive Bayes (GaussianNB) |
| naive_bayes.MultinomialNB([alpha, fit_prior]) | Naive Bayes classifier for multinomial models |
| naive_bayes.BernoulliNB([alpha, binarize, ...]) | Naive Bayes classifier for multivariate Bernoulli models. |
8.20. sklearn.neighbors: Nearest Neighbors¶
The sklearn.neighbors module implements the k-nearest neighbors algorithm.
User guide: See the Nearest Neighbors section for further details.
| neighbors.NearestNeighbors([n_neighbors, ...]) | Unsupervised learner for implementing neighbor searches. |
| neighbors.KNeighborsClassifier([...]) | Classifier implementing the k-nearest neighbors vote. |
| neighbors.RadiusNeighborsClassifier([...]) | Classifier implementing a vote among neighbors within a given radius |
| neighbors.KNeighborsRegressor([n_neighbors, ...]) | Regression based on k-nearest neighbors. |
| neighbors.RadiusNeighborsRegressor([radius, ...]) | Regression based on neighbors within a fixed radius. |
| neighbors.BallTree | Ball Tree for fast nearest-neighbor searches : |
| neighbors.kneighbors_graph(X, n_neighbors[, ...]) | Computes the (weighted) graph of k-Neighbors for points in X |
| neighbors.radius_neighbors_graph(X, radius) | Computes the (weighted) graph of Neighbors for points in X |
8.21. sklearn.pls: Partial Least Squares¶
The sklearn.pls module implements Partial Least Squares (PLS).
User guide: See the Partial Least Squares section for further details.
| pls.PLSRegression([n_components, scale, ...]) | PLS regression |
| pls.PLSCanonical([n_components, scale, ...]) | PLS canonical. PLSCanonical inherits from PLS with mode=”A” and |
| pls.CCA([n_components, scale, algorithm, ...]) | CCA Canonical Correlation Analysis. CCA inherits from PLS with |
| pls.PLSSVD([n_components, scale, copy]) | Partial Least Square SVD |
8.22. sklearn.pipeline: Pipeline¶
The sklearn.pipeline module implements utilites to build a composite estimator, as a chain of transforms and estimators.
| pipeline.Pipeline(steps) | Pipeline of transforms with a final estimator |
8.23. sklearn.preprocessing: Preprocessing and Normalization¶
The sklearn.preprocessing module implements various utilities to perform common data preprocessing steps (scaling, normalization, etc).
User guide: See the Preprocessing data section for further details.
| preprocessing.Scaler([copy, with_mean, with_std]) | Standardize features by removing the mean and scaling to unit variance |
| preprocessing.Normalizer([norm, copy]) | Normalize samples individually to unit norm |
| preprocessing.Binarizer([threshold, copy]) | Binarize data (set feature values to 0 or 1) according to a threshold |
| preprocessing.LabelBinarizer | Binarize labels in a one-vs-all fashion |
| preprocessing.KernelCenterer | Center a kernel matrix |
| preprocessing.scale(X[, axis, with_mean, ...]) | Standardize a dataset along any axis |
| preprocessing.normalize(X[, norm, axis, copy]) | Normalize a dataset along any axis |
| preprocessing.binarize(X[, threshold, copy]) | Boolean thresholding of array-like or scipy.sparse matrix |
8.24. sklearn.svm: Support Vector Machines¶
The sklearn.svm module includes Support Vector Machine algorithms.
User guide: See the Support Vector Machines section for further details.
8.24.1. For dense data¶
| svm.SVC([C, kernel, degree, gamma, coef0, ...]) | C-Support Vector Classification. |
| svm.LinearSVC([penalty, loss, dual, tol, C, ...]) | Linear Support Vector Classification. |
| svm.NuSVC([nu, kernel, degree, gamma, ...]) | Nu-Support Vector Classification. |
| svm.SVR([kernel, degree, gamma, coef0, tol, ...]) | epsilon-Support Vector Regression. |
| svm.NuSVR([nu, C, kernel, degree, gamma, ...]) | Nu Support Vector Regression. |
| svm.OneClassSVM([kernel, degree, gamma, ...]) | Unsupervised Outliers Detection. |
| svm.l1_min_c(X, y[, loss, fit_intercept, ...]) | Return the maximum value for C that yields a model with coefficients |
8.24.2. For sparse data¶
The sklearn.svm.sparse module includes Support Vector Machine algorithms for sparse matrices.
This module should have the same API as sklearn.svm, except that matrices are expected to be in some sparse format supported by scipy.sparse.
Note
Some fields, like dual_coef_ are not sparse matrices strictly speaking. However, they are converted to a sparse matrix for consistency and efficiency when multiplying to other sparse matrices.
| svm.sparse.SVC([C, kernel, degree, gamma, ...]) | SVC for sparse matrices (csr). |
| svm.sparse.NuSVC([nu, kernel, degree, ...]) | NuSVC for sparse matrices (csr). |
| svm.sparse.SVR([kernel, degree, gamma, ...]) | SVR for sparse matrices (csr) |
| svm.sparse.NuSVR([nu, C, kernel, degree, ...]) | NuSVR for sparse matrices (csr) |
| svm.sparse.OneClassSVM([kernel, degree, ...]) | OneClassSVM for sparse matrices (csr) |
| svm.sparse.LinearSVC([penalty, loss, dual, ...]) | Linear Support Vector Classification, Sparse Version |
8.24.3. Low-level methods¶
| svm.libsvm.fit | Train the model using libsvm (low-level method) |
| svm.libsvm.decision_function | Predict margin (libsvm name for this is predict_values) |
| svm.libsvm.predict | Predict target values of X given a model (low-level method) |
| svm.libsvm.predict_proba | Predict probabilities svm_model stores all parameters needed to predict a given value. |
| svm.libsvm.cross_validation | Binding of the cross-validation routine (low-level routine) |
8.25. sklearn.tree: Decision Trees¶
The sklearn.tree module includes decision tree-based models for classification and regression.
User guide: See the Decision Trees section for further details.
| tree.DecisionTreeClassifier([criterion, ...]) | A decision tree classifier. |
| tree.DecisionTreeRegressor([criterion, ...]) | A 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. |
8.26. sklearn.utils: Utilities¶
The sklearn.utils module includes various utilites.
Developer guide: See the Utilities for Developers page for further details.
| utils.check_random_state(seed) | Turn seed into a np.random.RandomState instance |
| utils.resample(*arrays, **options) | Resample arrays or sparse matrices in a consistent way |
| utils.shuffle(*arrays, **options) | Shuffle arrays or sparse matrices in a consistent way |