f1、精确度、准确度和召回率是否都有相同的值

Question

我一直在尝试使用scikit learn实现支持向量机算法，在进行一些测量后，所有分数都提供了相同的值。

x = df["Text"]
y = df["Mood"]
test_size = 5122
x_test = x[:-test_size]
y_test = y[:-test_size]
x_train = x[-test_size:]
y_train = y[-test_size:]
count_vect = CountVectorizer()
X_train_counts = count_vect.fit_transform(x_train)
tfidf_transformer = TfidfTransformer()
X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)
x_test = count_vect.transform(x_test).toarray()
SVM = svm.SVC(C=1.0, kernel='linear', degree=3, gamma='auto')
SVM.fit(X_train_tfidf, y_train)
predictions_SVM = SVM.predict(x_test)
print('Accuracy score is: ', accuracy_score(y_test, predictions_SVM))
print('F1 score is: ', f1_score(y_test, predictions_SVM, average='micro'))
print('Precission score is: ', precision_score(y_test, predictions_SVM, average ='micro'))
print('Recall score is: ', recall_score(y_test, predictions_SVM, average='micro'))

输出：

Accuracy score is:  0.9687622022647403
F1 score is:  0.9687622022647403
Precission score is:  0.9687622022647403
Recall score is:  0.9687622022647403

这正常吗？还是我在哪里出错了？

Answer 1

查看这些分数的文档，当您使用"micro"时，它们看起来应该都是一样的。

他们都在计算你得到正确标签的次数。

参见示例：

https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html

https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html

https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html

https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html

事实上，在最后三场比赛中，他们都给出了相同的例子，当然也得到了相同的分数。