我正在训练一个逻辑回归分类模型，并尝试使用混淆矩阵来比较结果，并计算精确率、召回率和准确率，代码如下所示
# 逻辑回归分类模型
clf_lr = sklearn.linear_model.LogisticRegression(penalty='l2', class_weight='balanced')
logistic_fit=clf_lr.fit(TrainX, np.where(TrainY >= delay_threshold,1,0))
pred = clf_lr.predict(TestX)
# 打印结果
cm_lr = confusion_matrix(np.where(TestY >= delay_threshold,1,0), pred)
print("Confusion matrix")
print(pd.DataFrame(cm_lr))
report_lr = precision_recall_fscore_support(list(np.where(TestY >= delay_threshold,1,0)), list(pred), average='micro')
print ("\nprecision = %0.2f, recall = %0.2f, F1 = %0.2f, accuracy = %0.2f\n" % \
(report_lr[0], report_lr[1], report_lr[2], accuracy_score(list(np.where(TestY >= delay_threshold,1,0)), list(pred))))
print(pd.DataFrame(cm_lr.astype(np.float64) / cm_lr.sum(axis=1)))
show_confusion_matrix(cm_lr)
#linear_score = cross_validation.cross_val_score(linear_clf, ArrX, ArrY,cv=10)
#print linear_score预期结果是
Confusion matrix 0 1
0 4303 2906
1 1060 1731
precision = 0.37, recall = 0.62, F1 = 0.47, accuracy = 0.60
0 1
0 0.596893 1.041204
1 0.147038 0.620208然而，我的输出是
Confusion matrix 0 1
0 4234 2891
1 1097 1778
precision = 0.60, recall = 0.60, F1 = 0.60, accuracy = 0.60
0 1
0 0.594246 1.005565
1 0.153965 0.618435如何获得正确的结果？
回答：
在像您这样的“二元”情况下（2个类别），您需要使用 average=’binary’ 而不是 average=’micro’。
例如：
TestY = [0, 1, 1, 0, 1, 1, 1, 0, 0, 0]
pred = [0, 1, 1, 0, 0, 1, 0, 1, 0, 0]
# 打印结果
cm_lr = metrics.confusion_matrix(TestY, pred)
print("Confusion matrix")
print(pd.DataFrame(cm_lr))
report_lr = metrics.precision_recall_fscore_support(TestY, pred, average='binary')
print ("\nprecision = %0.2f, recall = %0.2f, F1 = %0.2f, accuracy = %0.2f\n" % \
(report_lr[0], report_lr[1], report_lr[2], metrics.accuracy_score(TestY, pred)))输出如下：
Confusion matrix 0 1
0 4 1
1 2 3
precision = 0.75, recall = 0.60, F1 = 0.67, accuracy = 0.70二元分类有一个默认定义，哪个类别是正类（标记为1的类别）。您可以在此链接中阅读所有平均选项之间的差异。
