class sklearn.tree.DecisionTreeRegressor(criterion='mse', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1

The sklearn.preprocessing package provides several common utility functions and transformer classes to change raw feature vectors into a representation that

A comparison of a several classifiers in scikit-learn on synthetic datasets. The point of this example is to illustrate the nature of decision boundaries of different

sklearn.datasets.make_friedman1(n_samples=100, n_features=10, noise=0.0, random_state=None)

class sklearn.linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, scoring=None, cv=None, gcv_mode=None

This illustrates the datasets.make_multilabel_classification dataset generator. Each sample consists of counts of two features (up to

A tutorial exercise for using different SVM kernels. This exercise is used in the

class sklearn.manifold.Isomap(n_neighbors=5, n_components=2, eigen_solver='auto', tol=0, max_iter=None, path_method='auto', neighbors_algorithm='auto'

Warning This implementation is not intended for large-scale applications

sklearn.datasets.fetch_olivetti_faces(data_home=None, shuffle=False, random_state=0, download_if_missing=True)