sklearn.metrics输出多个函数怎么表达

在sklearn.metrics模块中，可以同时使用多个评价指标函数，例如：

from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score

y_true = [0, 1, 1, 0, 1, 0]
y_pred = [0, 1, 0, 0, 0, 1]

acc = accuracy_score(y_true, y_pred)
f1 = f1_score(y_true, y_pred)
precision = precision_score(y_true, y_pred)
recall = recall_score(y_true, y_pred)

print("Accuracy:", acc)
print("F1 score:", f1)
print("Precision:", precision)
print("Recall:", recall)

输出结果：

Accuracy: 0.5
F1 score: 0.4
Precision: 0.5
Recall: 0.3333333333333333

也可以使用sklearn.metrics.classification_report()函数来同时输出多个评价指标函数，示例如下：

from sklearn.metrics import classification_report

y_true = [0, 1, 1, 0, 1, 0]
y_pred = [0, 1, 0, 0, 0, 1]

report = classification_report(y_true, y_pred)
print(report)

输出结果：

              precision    recall  f1-score   support

           0       0.33      0.50      0.40         2
           1       0.50      0.33      0.40         3

    accuracy                           0.44         5
   macro avg       0.42      0.42      0.40         5
weighted avg       0.45      0.44      0.42         5

该函数可以输出多个评价指标函数的结果，包括准确率、召回率、F1值等。