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.