sklearn.metrics.label_ranking_loss(y_true, y_score, sample_weight=None)

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

class sklearn.decomposition.IncrementalPCA(n_components=None, whiten=False, copy=True, batch_size=None)

class sklearn.dummy.DummyClassifier(strategy='stratified', random_state=None, constant=None)

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

class sklearn.feature_selection.SelectPercentile(score_func=, percentile=10)

Evaluate the ability of k-means initializations strategies to make the algorithm convergence robust as measured by the relative

A 1D regression with decision tree. The

class sklearn.naive_bayes.GaussianNB(priors=None)

sklearn.feature_selection.chi2(X, y)