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

sklearn.isotonic.check_increasing(x, y)

class sklearn.multiclass.OutputCodeClassifier(estimator, code_size=1.5, random_state=None, n_jobs=1)

sklearn.datasets.load_iris(return_X_y=False)

sklearn.datasets.make_sparse_spd_matrix(dim=1, alpha=0.95, norm_diag=False, smallest_coef=0.1, largest_coef=0.9, random_state=None)

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

When performing classification one often wants to predict not only the class label, but also the associated probability. This probability gives some kind of confidence

sklearn.preprocessing.minmax_scale(X, feature_range=(0, 1), axis=0, copy=True)