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

class sklearn.linear_model.MultiTaskElasticNetCV(l1_ratio=0.5, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True

sklearn.neighbors.kneighbors_graph(X, n_neighbors, mode='connectivity', metric='minkowski', p=2, metric_params=None, include_self=False

class sklearn.gaussian_process.GaussianProcessClassifier(kernel=None, optimizer='fmin_l_bfgs_b', n_restarts_optimizer=0

sklearn.metrics.hamming_loss(y_true, y_pred, labels=None, sample_weight=None, classes=None)

class sklearn.gaussian_process.kernels.PairwiseKernel(gamma=1.0, gamma_bounds=(1e-05, 100000.0), metric='linear',

sklearn.ensemble.partial_dependence.partial_dependence(gbrt, target_variables, grid=None, X=None, percentiles=(0

class sklearn.feature_selection.SelectFdr(score_func=, alpha=0.05)

sklearn.metrics.f1_score(y_true, y_pred, labels=None, pos_label=1, average='binary', sample_weight=None)

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