sklearn.metrics.average_precision_score(y_true, y_score, *, average='macro', pos_label=1, sample_weight=None)[source]

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:

\[\text{AP} = \sum_n (R_n - R_{n-1}) P_n\]

where \(P_n\) and \(R_n\) are the precision and recall at the nth threshold [1]. This implementation is not interpolated and is different from computing the area under the precision-recall curve with the trapezoidal rule, which uses linear interpolation and can be too optimistic.

Read more in the User Guide.

y_truearray-like of shape (n_samples,) or (n_samples, n_classes)

True binary labels or binary label indicators.

y_scorearray-like of 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).

average{‘micro’, ‘samples’, ‘weighted’, ‘macro’} or None, default=’macro’

If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data:


Calculate metrics globally by considering each element of the label indicator matrix as a label.


Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.


Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label).


Calculate metrics for each instance, and find their average.

Will be ignored when y_true is binary.

pos_labelint, float, bool or str, default=1

The label of the positive class. Only applied to binary y_true. For multilabel-indicator y_true, pos_label is fixed to 1.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.


Average precision score.

See also


Compute the area under the ROC curve.


Compute precision-recall pairs for different probability thresholds.


Changed in version 0.19: Instead of linearly interpolating between operating points, precisions are weighted by the change in recall since the last operating point.



>>> import numpy as np
>>> from sklearn.metrics import average_precision_score
>>> y_true = np.array([0, 0, 1, 1])
>>> y_scores = np.array([0.1, 0.4, 0.35, 0.8])
>>> average_precision_score(y_true, y_scores)
>>> y_true = np.array([0, 0, 1, 1, 2, 2])
>>> y_scores = np.array([
...     [0.7, 0.2, 0.1],
...     [0.4, 0.3, 0.3],
...     [0.1, 0.8, 0.1],
...     [0.2, 0.3, 0.5],
...     [0.4, 0.4, 0.2],
...     [0.1, 0.2, 0.7],
... ])
>>> average_precision_score(y_true, y_scores)

Examples using sklearn.metrics.average_precision_score