metrics. average_precision_score

metrics.average_precision_score(y_true, y_score, sample_weight=None)

Compute average precision (AP) from prediction scores

AP summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold, with the increase in recall from the previous threshold used as the weight:

Parameters:	y_true : array, shape = [n_samples] or [n_samples, n_classes] True binary labels or binary label indicators. y_score : array, shape = [n_samples] or [n_samples, n_classes] Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of decisions (as returned by "decision_function" on some classifiers). sample_weight : array-like of shape = [n_samples], optional Sample weights.
Returns:	average_precision : float

Parameters:

y_true : array, shape = [n_samples] or [n_samples, n_classes]: True binary labels or binary label indicators.
y_score : array, shape = [n_samples] or [n_samples, n_classes]: Target scores, can either be probability estimates of the positive
class, confidence values, or non-thresholded measure of decisions
(as returned by "decision_function" on some classifiers).
sample_weight : array-like of shape = [n_samples], optional: Sample weights.

Returns:

average_precision : float