average_precision_score#

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 precisionsachieved at each threshold, with the increase in recall from the previousthreshold 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 nththreshold[1]. This implementation is not interpolated and is differentfrom computing the area under the precision-recall curve with thetrapezoidal rule, which uses linear interpolation and can be toooptimistic.

Read more in theUser Guide.

Parameters:

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 positiveclass, confidence values, or non-thresholded measure of decisions(as returned bydecision_function on some classifiers).Fordecision_function scores, values greater than or equal tozero should indicate the positive class.

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

IfNone, the scores for each class are returned. Otherwise,this determines the type of averaging performed on the data:

'micro':

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

'macro':

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

'weighted':

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

'samples':

Calculate metrics for each instance, and find their average.

Will be ignored wheny_true is binary.

pos_labelint, float, bool or str, default=1

The label of the positive class. Only applied to binaryy_true.For multilabel-indicatory_true,pos_label is fixed to 1.

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

Sample weights.

Returns:

average_precisionfloat

Average precision score.

See also

roc_auc_score

Compute the area under the ROC curve.

precision_recall_curve

Compute precision-recall pairs for different probability thresholds.

PrecisionRecallDisplay.from_estimator

Plot the precision recall curve using an estimator and data.

PrecisionRecallDisplay.from_predictions

Plot the precision recall curve using true and predicted labels.

Notes

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

References

[1]

Wikipedia entry for the Average precision

Examples

>>>importnumpyasnp>>>fromsklearn.metricsimportaverage_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)0.83>>>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)0.77

Gallery examples#

Precision-Recall

Precision-Recall