class sklearn.linear_model.LassoLarsIC(criterion='aic', fit_intercept=True, verbose=False, normalize=True, precompute='auto', max_iter=500

class sklearn.covariance.GraphLassoCV(alphas=4, n_refinements=4, cv=None, tol=0.0001, enet_tol=0.0001, max_iter=100, mode='cd', n_jobs=1

sklearn.model_selection.validation_curve(estimator, X, y, param_name, param_range, groups=None, cv=None, scoring=None,

sklearn.datasets.fetch_20newsgroups_vectorized(subset='train', remove=(), data_home=None)

Support vector machines (SVMs) are a set of supervised learning methods used for

class sklearn.mixture.BayesianGaussianMixture(n_components=1, covariance_type='full', tol=0.001, reg_covar=1e-06, max_iter=100

sklearn.utils.estimator_checks.check_estimator(Estimator)

sklearn.metrics.pairwise.polynomial_kernel(X, Y=None, degree=3, gamma=None, coef0=1)

sklearn.model_selection.permutation_test_score(estimator, X, y, groups=None, cv=None, n_permutations=100, n_jobs=1

This example illustrates the differences between univariate F-test statistics and mutual information. We consider 3 features x_1, x_2, x_3