Scikit学习，获取每个班级的准确性得分 [英] Scikit-learn, get accuracy scores for each class

问题描述

是否有内置的方法可以分别获取每个班级的准确性得分?我知道在sklearn中，我们可以使用metric.accuracy_score来获得整体准确性.有没有一种方法可以细分各个类别的准确性得分?类似于metrics.classification_report.

Is there a built-in way for getting accuracy scores for each class separatetly? I know in sklearn we can get overall accuracy by using metric.accuracy_score. Is there a way to get the breakdown of accuracy scores for individual classes? Something similar to metrics.classification_report.

from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score

y_true = [0, 1, 2, 2, 2]
y_pred = [0, 0, 2, 2, 1]
target_names = ['class 0', 'class 1', 'class 2']

classification_report没有给出准确性得分:

classification_report does not give accuracy scores:

print(classification_report(y_true, y_pred, target_names=target_names, digits=4))

Out[9]:         precision    recall  f1-score   support

class 0     0.5000    1.0000    0.6667         1
class 1     0.0000    0.0000    0.0000         1
class 2     1.0000    0.6667    0.8000         3

avg / total     0.7000    0.6000    0.6133         5

准确度评分仅给出总体准确度:

Accuracy score gives only the overall accuracy:

accuracy_score(y_true, y_pred)
Out[10]: 0.59999999999999998

推荐答案

您可以使用sklearn的混淆矩阵以获取准确性

You can use sklearn's confusion matrix to get the accuracy

from sklearn.metrics import confusion_matrix
import numpy as np

y_true = [0, 1, 2, 2, 2]
y_pred = [0, 0, 2, 2, 1]
target_names = ['class 0', 'class 1', 'class 2']

#Get the confusion matrix
cm = confusion_matrix(y_true, y_pred)
#array([[1, 0, 0],
#   [1, 0, 0],
#   [0, 1, 2]])

#Now the normalize the diagonal entries
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
#array([[1.        , 0.        , 0.        ],
#      [1.        , 0.        , 0.        ],
#      [0.        , 0.33333333, 0.66666667]])

#The diagonal entries are the accuracies of each class
cm.diagonal()
#array([1.        , 0.        , 0.66666667])

参考

• 绘制混淆矩阵sklearn

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