sklearn.metrics.hinge_loss(y_true, pred_decision, labels=None, sample_weight=None)

This example applies to The Olivetti faces dataset different unsupervised matrix decomposition (dimension reduction) methods from

An example of estimating sources from noisy data.

class sklearn.ensemble.AdaBoostClassifier(base_estimator=None, n_estimators=50, learning_rate=1.0, algorithm='SAMME.R', random_state=None)

sklearn.model_selection.check_cv(cv=3, y=None, classifier=False)

Comparison of different linear SVM classifiers on a 2D projection of the iris dataset. We only consider the first 2 features of this dataset:

Transform a signal as a sparse combination of Ricker wavelets. This example visually compares different sparse coding methods using the

class sklearn.svm.SVR(kernel='rbf', degree=3, gamma='auto', coef0=0.0, tol=0.001, C=1.0, epsilon=0.1, shrinking=True, cache_size=200, verbose=False

Reference: Dorin Comaniciu and Peter Meer, ?Mean Shift: A robust approach toward feature space analysis?. IEEE Transactions on Pattern Analysis

sklearn.feature_selection.f_classif(X, y)