如何找到5种不同分类的混淆矩阵？

Question

我用它来获得具有 5 个不同分类的混淆矩阵

y_test_non_category = [ np.argmax(t) for t in y_test ]
y_predict_non_category = [ np.argmax(t) for t in y_pred ]

from sklearn.metrics import confusion_matrix
conf_mat = confusion_matrix(y_test_non_category, y_predict_non_category)

并得到

array([[ 76, 152,   4, 130,  56, 224],
       [  0,   0,   0,   0,   0,   0],
       [  0,   0,   0,   0,   0,   0],
       [  0,   0,   0,   0,   0,   0],
       [  0,   0,   0,   0,   0,   0],
       [  0,   0,   0,   0,   0,   0]])

如何计算精度和 F1 分数？ 混淆矩阵法

Answer 1

您可以根据需要使用sklearn.metrics.classification_report报告获取相应的指标。

>>> y_true = [0, 1, 2, 2, 2]
>>> y_pred = [0, 0, 2, 2, 1]
>>> target_names = ['class 0', 'class 1', 'class 2']
>>> print(classification_report(y_true, y_pred, target_names=target_names))
              precision    recall  f1-score   support
<BLANKLINE>
     class 0       0.50      1.00      0.67         1
     class 1       0.00      0.00      0.00         1
     class 2       1.00      0.67      0.80         3
<BLANKLINE>
    accuracy                           0.60         5
   macro avg       0.50      0.56      0.49         5
weighted avg       0.70      0.60      0.61         5
<BLANKLINE>
>>> y_pred = [1, 1, 0]
>>> y_true = [1, 1, 1]
>>> print(classification_report(y_true, y_pred, labels=[1, 2, 3]))
              precision    recall  f1-score   support
<BLANKLINE>
           1       1.00      0.67      0.80         3
           2       0.00      0.00      0.00         0
           3       0.00      0.00      0.00         0
<BLANKLINE>
   micro avg       1.00      0.67      0.80         3
   macro avg       0.33      0.22      0.27         3
weighted avg       1.00      0.67      0.80         3