6. Class reference¶
6.1. Support Vector Machines¶
| svm.SVC([C, kernel, degree, gamma, coef0, ...]) | C-Support Vector Classification. |
| svm.LinearSVC([penalty, loss, dual, eps, C, ...]) | Linear Support Vector Classification. |
| svm.NuSVC([nu, kernel, degree, gamma, ...]) | Nu-Support Vector Classification. |
| svm.SVR([kernel, degree, gamma, coef0, ...]) | Support Vector Regression. |
| svm.NuSVR([nu, C, kernel, degree, gamma, ...]) | Nu Support Vector Regression. |
| svm.OneClassSVM([kernel, degree, gamma, ...]) | Unsupervised Outliers Detection. |
6.1.7. For sparse data¶
| svm.sparse.SVC([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, ...]) | NuSVR for sparse matrices (csr) |
| svm.sparse.LinearSVC([penalty, loss, dual, ...]) | Linear Support Vector Classification, Sparse Version |
6.2. Generalized Linear Models¶
| linear_model.LinearRegression([fit_intercept]) | Ordinary least squares Linear Regression. |
| linear_model.Ridge([alpha, fit_intercept]) | Ridge regression. |
| 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.LogisticRegression([penalty, ...]) | Logistic Regression. |
| 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.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 |
6.2.15. For sparse data¶
| linear_model.sparse.Lasso([alpha, fit_intercept]) | 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 |
6.3. Bayesian Regression¶
| linear_model.BayesianRidge([n_iter, eps, ...]) | Bayesian ridge regression |
| linear_model.ARDRegression([n_iter, eps, ...]) | Bayesian ARD regression. |
6.4. Naive Bayes¶
| naive_bayes.GNB() | Gaussian Naive Bayes (GNB) |
6.5. Nearest Neighbors¶
| neighbors.NeighborsClassifier([n_neighbors, ...]) | Classifier implementing k-Nearest Neighbor Algorithm. |
| neighbors.NeighborsRegressor([n_neighbors, ...]) | Regression based on k-Nearest Neighbor Algorithm. |
| ball_tree.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 |
| ball_tree.knn_brute(x, pt[, k]) | Brute-Force k-nearest neighbor search. |
6.6. Gaussian Mixture Models¶
| mixture.GMM([n_states, cvtype]) | Gaussian Mixture Model |
6.8. Clustering¶
| cluster.KMeans([k, init, n_init, max_iter, ...]) | K-Means clustering |
| cluster.MeanShift([bandwidth]) | MeanShift clustering |
| cluster.SpectralClustering([k, mode]) | Spectral clustering: apply k-means to a projection of the graph laplacian, finds normalized graph cuts. |
| cluster.AffinityPropagation([damping, ...]) | Perform Affinity Propagation Clustering of data |
6.9. Metrics¶
| metrics.euclidean_distances(X, Y[, ...]) | Considering the rows of X (and Y=X) as vectors, compute the |
| metrics.unique_labels | |
| metrics.confusion_matrix(y_true, y_pred[, ...]) | Compute confusion matrix to evaluate the accuracy of a classification |
| metrics.roc_curve(y, probas_) | 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.r2_score(y_true, y_pred) | R^2 (coefficient of determination) regression score function |
| metrics.zero_one_score(y_true, y_pred) | Zero-One classification score |
| metrics.zero_one(y_true, y_pred) | Zero-One classification loss |
| metrics.mean_square_error(y_true, y_pred) | Mean square error regression loss |
6.10. Covariance Estimators¶
| covariance.Covariance([store_covariance]) | Basic covariance estimator |
| covariance.ShrunkCovariance([...]) | Covariance estimator with shrinkage |
| covariance.LedoitWolf([store_covariance]) | LedoitWolf Estimator |
| covariance.ledoit_wolf(X[, return_shrinkage]) | Estimates the shrunk Ledoit-Wolf covariance matrix. |
6.11. Signal Decomposition¶
| pca.PCA([n_components, copy, whiten]) | Principal component analysis (PCA) |
| pca.ProbabilisticPCA([n_components, copy, ...]) | |
| pca.RandomizedPCA(n_components[, copy, ...]) | Principal component analysis (PCA) using randomized SVD |
| fastica.FastICA([n_components, algorithm, ...]) | FastICA; a fast algorithm for Independent Component Analysis |
| fastica.fastica(X[, n_components, ...]) | Perform Fast Independent Component Analysis. |
6.12. Cross Validation¶
| cross_val.LeaveOneOut(n[, indices]) | Leave-One-Out cross validation iterator |
| cross_val.LeavePOut(n, p[, indices]) | Leave-P-Out cross validation iterator |
| cross_val.KFold(n, k[, indices]) | K-Folds cross validation iterator |
| cross_val.StratifiedKFold(y, k[, indices]) | Stratified K-Folds cross validation iterator |
| cross_val.LeaveOneLabelOut(labels[, indices]) | Leave-One-Label_Out cross-validation iterator |
| cross_val.LeavePLabelOut(labels, p[, indices]) | Leave-P-Label_Out cross-validation iterator |
6.13. Grid Search¶
| grid_search.GridSearchCV(estimator, param_grid) | Grid search on the parameters of a classifier |
6.14. Feature Selection¶
| feature_selection.rfe.RFE([estimator, ...]) | Feature ranking with Recursive feature elimination |
| feature_selection.rfe.RFECV([estimator, ...]) | Feature ranking with Recursive feature elimination and cross validation |
6.15. Feature Extraction¶
| feature_extraction.image.img_to_graph(img[, ...]) | Graph of the pixel-to-pixel gradient connections |
| feature_extraction.text.RomanPreprocessor | Fast preprocessor suitable for roman languages .. |
| feature_extraction.text.WordNGramAnalyzer([...]) | Simple analyzer: transform a 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 TF or TF-IDF representation |
| feature_extraction.text.Vectorizer(...) | Convert a collection of raw documents to a matrix |
6.15.8. For sparse data¶
| feature_extraction.text.sparse.TfidfTransformer([...]) | |
| feature_extraction.text.sparse.CountVectorizer(...) | Convert a collection of raw documents to a matrix of token counts |
| feature_extraction.text.sparse.Vectorizer(...) | Convert a collection of raw documents to a sparse matrix |
6.16. Pipeline¶
| pipeline.Pipeline(steps) | Pipeline of transforms with a final estimator |