sklearn.metrics.label_ranking_average_precision_score(y_true, y_score)

sklearn.preprocessing.label_binarize(y, classes, neg_label=0, pos_label=1, sparse_output=False)

class sklearn.decomposition.FastICA(n_components=None, algorithm='parallel', whiten=True, fun='logcosh', fun_args=None, max_iter=200

An example using IsolationForest for anomaly detection. The IsolationForest ?isolates? observations by randomly selecting a feature and then randomly selecting

This is the class and function reference of scikit-learn. Please refer to the full user guide for further details, as the class and function raw specifications may

sklearn.metrics.pairwise_distances_argmin(X, Y, axis=1, metric='euclidean', batch_size=500, metric_kwargs=None)

sklearn.metrics.hinge_loss(y_true, pred_decision, labels=None, sample_weight=None)

Plot the contours of the three penalties. All of the above are supported by sklearn.linear_model.stochastic_gradient.

Often the hardest part of solving a machine learning problem can be finding the right estimator for the job. Different estimators are better suited for different types of data and different

class sklearn.base.ClusterMixin