Warning DEPRECATED

class sklearn.covariance.OAS(store_precision=True, assume_centered=False)

class sklearn.linear_model.SGDClassifier(loss='hinge', penalty='l2', alpha=0.0001, l1_ratio=0.15, fit_intercept=True, n_iter=5,

sklearn.svm.libsvm.predict_proba() Predict probabilities svm_model stores all parameters needed to

class sklearn.decomposition.TruncatedSVD(n_components=2, algorithm='randomized', n_iter=5, random_state=None, tol=0.0)

Demonstrate the resolution of a regression problem using a k-Nearest Neighbor and the interpolation of the target using both barycenter and constant weights.

sklearn.datasets.fetch_20newsgroups(data_home=None, subset='train', categories=None, shuffle=True, random_state=42, remove=()

class sklearn.linear_model.MultiTaskLasso(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=1000, tol=0.0001

An example to illustrate multi-output regression with decision tree. The

class sklearn.tree.ExtraTreeClassifier(criterion='gini', splitter='random', max_depth=None, min_samples_split=2, min_samples_leaf=1