sklearn中,二分类的precision |
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precision_score:精确率,查准率 P = T P T P + F P P=\frac{TP}{TP+FP} P=TP+FPTP # 假设二分类标签为1,2 from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score precision_score(y_true, y_pred, average="binary", pos_label=1) # pos_label设置为1,代表标签为1的样本是正例,标签为2的样本是负例。accuracy_score:准确率 A c c = T P + T N T P + F P + T N + F N Acc=\frac{TP+TN}{TP+FP+TN+FN} Acc=TP+FP+TN+FNTP+TN # 假设二分类标签为1,2 accuracy_score(y_true, y_pred)recall_score:召回率,查全率 R = T P T P + F N R=\frac{TP}{TP+FN} R=TP+FNTP # 假设二分类标签为1,2 recall_score(y_true, y_pred, average="binary", pos_label=1)f1_score: F1值 F 1 = 2 ∗ P ∗ R P + R F1=\frac{2*P*R}{P+R} F1=P+R2∗P∗R # 假设二分类标签为1,2 f1_score(y_true, y_pred, average="binary", pos_label=1)附:二分类的混淆矩阵 真实情况预测结果正例反例正例TP(真正例)FN(假反例)反例FP(假正例)TN(真反例) |
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