我在keras中训练了一个模型，并进行了一些预测。为了评估模型的性能，我使用sklearn库计算了精度和召回率得分以及混淆矩阵。这是我的代码：
final_predictions = model.predict_generator(generator_test, steps=steps_per_epoch_test)rounded_pred = [0 if x<=0.5 else 1 for x in final_predictions]test_precision_score = round(precision_score(y_test, rounded_pred), 3)test_recall_score = round(recall_score(y_test, rounded_pred), 3)test_confusion_matrix = confusion_matrix(y_test, rounded_pred)这些是我的结果：
Test confusion matrix : [[1555 13] [ 9 49]]Precision and recall:Test Precision :: 0.845Test Recall :: 0.79有人知道为什么精度得分计算错误吗？（它应该是1555/(1555+13)而不是(13/(13+49))）
回答：
precision_score和recall_score的默认pos_label是1。
from sklearn.metrics import confusion_matrix,precision_score,recall_score,classification_reporty_true = [0]*1568 + [1]*58y_pred = [0]*1555 + [1]*13 + [0]* 9+ [1]* 49print('confusion matrix :\n',confusion_matrix(y_true,y_pred))print('precision_score :\n',precision_score(y_true,y_pred,pos_label=1))print('recall_score :\n',recall_score(y_true,y_pred,pos_label=1))print('classification_report :\n',classification_report(y_true,y_pred))confusion matrix : [[1555 13] [ 9 49]]precision_score : 0.7903225806451613recall_score : 0.8448275862068966classification_report : precision recall f1-score support 0 0.99 0.99 0.99 1568 1 0.79 0.84 0.82 58 micro avg 0.99 0.99 0.99 1626 macro avg 0.89 0.92 0.90 1626weighted avg 0.99 0.99 0.99 1626如果你想得到label=1的precision_score和recall_score，你可以设置pos_label=0来指定类别。
print('precision_score :\n',precision_score(y_true,y_pred,pos_label=0))print('recall_score :\n',recall_score(y_true,y_pred,pos_label=0))precision_score : 0.9942455242966752recall_score : 0.9917091836734694