Question

我正在训练逻辑回归分类模型并尝试使用混淆矩阵比较结果，并计算精度，召回率，准确度代码如下：

# logistic regression classification model
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)

# print results
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

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？

Answer 1

在像你这样的'二进制'情况下（2个类）你需要使用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]
# print results
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)))

和输出：

Confusion matrix
   0  1
0  4  1
1  2  3

precision = 0.75, recall = 0.60, F1 = 0.67, accuracy = 0.70

二进制有一个默认定义，哪个类是正类（具有1个标签的类）。您可以阅读此link中所有平均选项之间的差异。

precision_recall_fscore_support返回相同的精度，精度和召回值

1 个答案: