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 precisionrecall curve as the weighted mean of precisions achieved at each threshold, with the increase in recall from the previous threshold used as the weight:
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 precisionrecall curve with the trapezoidal rule, which uses linear interpolation and can be too optimistic.
Note: this implementation is restricted to the binary classification task or multilabel classification task.
Read more in the User Guide.
Parameters: 


Returns: 

See also
roc_auc_score
precision_recall_curve
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.
[1]  (1, 2) Wikipedia entry for the Average precision 
>>> 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) 0.83...
sklearn.metrics.average_precision_score
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http://scikitlearn.org/stable/modules/generated/sklearn.metrics.average_precision_score.html