The usual covariance maximum likelihood estimate is very sensitive to the presence of outliers in the data set. In such a case, it would be better to

sklearn.metrics.roc_curve(y_true, y_score, pos_label=None, sample_weight=None, drop_intermediate=True)

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

sklearn.cluster.spectral_clustering(affinity, n_clusters=8, n_components=None, eigen_solver=None, random_state=None, n_init=10

class sklearn.ensemble.RandomForestRegressor(n_estimators=10, criterion='mse', max_depth=None, min_samples_split=2, min_samples_leaf=1

class sklearn.neighbors.KernelDensity(bandwidth=1.0, algorithm='auto', kernel='gaussian', metric='euclidean', atol=0, rtol=0, breadth_first=True

The dataset used in this example is the 20 newsgroups dataset which will be automatically downloaded and then cached and reused for

sklearn.datasets.load_boston(return_X_y=False)

The goal of ensemble methods is to combine the predictions of several base estimators built with a given learning algorithm in order to improve generalizability

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