For some applications the amount of examples, features (or both) and/or the speed at which they need to be processed are challenging for traditional

Warning DEPRECATED

The plots display firstly what a K-means algorithm would yield using three clusters. It is then shown what the effect of a bad initialization is on the classification process:

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

sklearn.svm.libsvm.predict() Predict target values of X given a model (low-level method)

sklearn.feature_selection.f_classif(X, y)

Statistical learning

class sklearn.multioutput.MultiOutputRegressor(estimator, n_jobs=1)

sklearn.datasets.get_data_home(data_home=None)

This example shows how to perform univariate feature selection before running a SVC (support vector classifier) to improve the classification