class sklearn.model_selection.GroupShuffleSplit(n_splits=5, test_size=0.2, train_size=None, random_state=None)

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

class sklearn.linear_model.TheilSenRegressor(fit_intercept=True, copy_X=True, max_subpopulation=10000.0, n_subsamples=None,

class sklearn.cluster.MeanShift(bandwidth=None, seeds=None, bin_seeding=False, min_bin_freq=1, cluster_all=True, n_jobs=1)

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

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

sklearn.metrics.median_absolute_error(y_true, y_pred)

class sklearn.kernel_approximation.RBFSampler(gamma=1.0, n_components=100, random_state=None)

class sklearn.neighbors.KDTree KDTree for fast generalized N-point problems KDTree(X, leaf_size=40, metric=?minkowski

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