sklearn.metrics中precision_recall_curve

可以使用sklearn.metrics中的precision_recall_curve函数来计算precision-recall曲线。该函数的参数包括y_true（真实标签）、probas_pred（预测概率值）、pos_label（正类标签，默认为None）和sample_weight（样本权重，默认为None）\[1\]。该函数会返回precision（精确率）、recall（召回率）和thresholds（阈值）三个数组\[3\]。你可以通过导入precision_recall_curve函数并传入相应的参数来计算PR曲线\[3\]。例如，你可以使用以下代码计算PR曲线：

from sklearn.metrics import precision_recall_curve

y_true = \[0, 0, 1, 1\]
y_score = \[0.1, 0.4, 0.35, 0.8\]

precision, recall, thresholds = precision_recall_curve(y_true, y_score)

print(precision)
print(recall)
print(thresholds)

运行以上代码会输出precision、recall和thresholds的值\[3\]。
#### 引用[.reference_title]
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