RuleTree.evaluation.evaluate_classification

evaluate_classification(y_true, y_pred, y_proba=None, labels=None) → dict

Accuracy, F1, precision and recall of the predictions of a classifier and, with the class probabilities, ROC AUC and average precision.

F1, precision and recall are given with the micro, macro and weighted averages; f1_score, precision_score and recall_score are the ones of the positive class (the second one) for binary problems, and the micro averages otherwise.

Parameters:

• y_true (array-like of shape (n_samples,)) – True classes.

• y_pred (array-like of shape (n_samples,)) – Predicted classes.

• y_proba (array-like of shape (n_samples, n_classes), default=None) – Predicted class probabilities (e.g. predict_proba), in the order of labels. If None, the metrics based on probabilities are not computed.

• labels (array-like of shape (n_classes,), default=None) – Classes, in the order of the columns of y_proba (e.g. model.classes_). If None, the sorted classes of y_true.

Returns:

scores – Metric name -> value.

Return type:

dict

See also

sklearn.metrics

the definitions of the metrics.

Examples

>>> from sklearn.datasets import load_iris
>>> from RuleTree import RuleTreeClassifier
>>> from RuleTree.evaluation import evaluate_classification
>>> X, y = load_iris(return_X_y=True)
>>> model = RuleTreeClassifier(max_depth=2, random_state=0).fit(X, y)
>>> scores = evaluate_classification(y, model.predict(X), model.predict_proba(X), labels=model.classes_)
>>> round(scores["accuracy"], 3), round(scores["roc_macro"], 3)
(0.96, 0.98)