Classification

This module contains algorithms for solving classification problems.

classification.AdaGradClassifier([eta, ...]) Estimator for learning linear classifiers by AdaGrad.
classification.CDClassifier([loss, penalty, ...]) Estimator for learning linear classifiers by (block) coordinate descent.
classification.FistaClassifier([C, alpha, ...]) Estimator for learning linear classifiers by FISTA.
classification.KernelSVC([alpha, solver, ...]) Estimator for learning kernel SVMs by Newton’s method.
classification.LinearSVC([C, loss, ...]) Estimator for learning linear support vector machine by coordinate descent in the dual.
classification.SDCAClassifier([alpha, ...]) Estimator for learning linear classifiers by (proximal) SDCA.
classification.SAGClassifier([eta, alpha, ...]) Estimator for learning linear classifiers by SAG.
classification.SAGAClassifier([eta, alpha, ...]) Estimator for learning linear classifiers by SAGA.
classification.SGDClassifier([loss, ...]) Estimator for learning linear classifiers by SGD.
classification.SVRGClassifier([eta, alpha, ...]) Estimator for learning linear classifiers by SVRG.

Regression

This module contains algorithms for solving regression problems.

regression.AdaGradRegressor([eta, alpha, ...]) Estimator for learning linear regressors by AdaGrad.
regression.CDRegressor([C, alpha, loss, ...]) Estimator for learning linear regressors by (block) coordinate descent.
regression.FistaRegressor([C, alpha, ...]) Estimator for learning linear classifiers by FISTA.
regression.LinearSVR([C, epsilon, loss, ...]) Estimator for learning a linear support vector regressor by coordinate descent in the dual.
regression.SAGRegressor([eta, alpha, beta, ...]) Estimator for learning linear regressors by SAG.
regression.SAGARegressor([eta, alpha, beta, ...]) Estimator for learning linear regressors by SAG.
regression.SDCARegressor([alpha, l1_ratio, ...]) Estimator for learning linear regressors by (proximal) SDCA.
regression.SGDRegressor([loss, penalty, ...]) Estimator for learning linear regressors by SGD.
regression.SVRGRegressor([eta, alpha, loss, ...]) Estimator for learning linear regressors by SVRG.

Ranking

This module contains algorithms for solving ranking and ordinal regression problems.

ranking.PRank([n_iter, shuffle, random_state]) Online algorithm for learning an ordinal regression model.
ranking.KernelPRank([n_iter, shuffle, ...]) Kernelized online algorithm for learning an ordinal regression model.