sklearn.metrics.precision_recall_fscore_support(y_true, y_pred, beta=1.0, labels=None, pos_label=1, average=None

sklearn.datasets.make_classification(n_samples=100, n_features=20, n_informative=2, n_redundant=2, n_repeated=0, n_classes=2

class sklearn.exceptions.ChangedBehaviorWarning

When performing classification you often want not only to predict the class label, but also obtain a probability of the respective label. This probability gives you

class sklearn.pipeline.Pipeline(steps)

Warning DEPRECATED

class sklearn.model_selection.ParameterGrid(param_grid)

sklearn.datasets.make_swiss_roll(n_samples=100, noise=0.0, random_state=None)

class sklearn.model_selection.GroupKFold(n_splits=3)

class sklearn.neighbors.DistanceMetric DistanceMetric class This class provides a uniform interface to