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文章目录

• • 1.classification_report
  • 2.计算过程
  • 3.Macro Average
  • 4.Weighted Average
  • 5.Micro Average
  • 参考资料

1.classification_report

sklearn.metrics.classification_report输出分类预测结果的常用评估标准，输入是标签和类别的预测向量，包括精准度，召回率和F1 Score。

借用1个例子来说明一下计算过程。原例子见：

https://towardsdatascience.com/micro-macro-weighted-averages-of-f1-score-clearly-explained-b603420b292f

数据为：

from sklearn.metrics import classification_report
import numpy as np
names = ["Airplane", "Boat", "Car"]
names.sort()
class_dict = dict([[v, i] for i,v in enumerate(names)])
# {'Airplane': 0, 'Boat': 1, 'Car': 2}
tru = np.array([0, 2, 2, 2, 2, 0, 1, 2, 0, 2])
pre = np.array([0, 1, 2, 2, 1, 1, 1, 0, 0, 2])
report = classification_report(tru, pre)
print(report)

输出结果为：

              precision    recall  f1-score   support

           0       0.67      0.67      0.67         3
           1       0.25      1.00      0.40         1
           2       1.00      0.50      0.67         6

    accuracy                           0.60        10
   macro avg       0.64      0.72      0.58        10
weighted avg       0.82      0.60      0.64        10

2.计算过程

上述预测结果对应的混淆矩阵为：

每个类别对应的TP(True Positive)\FP(False Negative)\FN(False Negative)为：

计算每个类别的Precision, Recall和F1 Score:

上述的结果对每个类别单个输出，如果要输出分类效果的整体指标，最好的办法就是对每个类别做平均。Macro Average,Weighted Average,Micro Average对应不同的平均方法。

3.Macro Average

Macro Average是直接对各指标求和求平均。

m a c r o _ a v g _ p r e c i s i o n = 0.67 + 0.25 + 1 3 = 0.64 macro\_avg\_precision = \frac{0.67 + 0.25 + 1}{3} = 0.64 macro_avg_precision=30.67+0.25+1​=0.64
m a c r o _ a v g _ r e c a l l = 0.67 + 1.00 + 0.50 3 = 0.72 macro\_avg\_recall = \frac{0.67 + 1.00 + 0.50}{3} = 0.72 macro_avg_recall=30.67+1.00+0.50​=0.72
m a c r o _ a v g _ f 1 − s c o r e = 0.67 + 0.40 + 0.67 3 = 0.58 macro\_avg\_f1-score = \frac{0.67 + 0.40 + 0.67}{3} = 0.58 macro_avg_f1−score=30.67+0.40+0.67​=0.58

在处理不平衡数据时，可以使用Macro Average衡量算法的效果。

4.Weighted Average

Weighted Average的计算方式为：

w e i g h t e d _ a v g _ p r e c i s i o n = 0.67 ∗ 0.3 + 0.25 ∗ 0.1 + 1.00 ∗ 0.67 = 0.82 weighted\_avg\_precision = 0.67*0.3 + 0.25 * 0.1 + 1.00 * 0.67 = 0.82 weighted_avg_precision=0.67∗0.3+0.25∗0.1+1.00∗0.67=0.82

w e i g h t e d _ a v g _ r e c a l l = 0.67 ∗ 0.3 + 1.0 ∗ 0.1 + 0.5 ∗ 0.6 = 0.6 weighted\_avg\_recall = 0.67*0.3 + 1.0 * 0.1 + 0.5 * 0.6 = 0.6 weighted_avg_recall=0.67∗0.3+1.0∗0.1+0.5∗0.6=0.6

w e i g h t e d _ a v g _ f 1 − s c o r e = 0.67 ∗ 0.3 + 0.4 ∗ 0.1 + 0.67 ∗ 0.6 = 0.64 weighted\_avg\_f1-score = 0.67*0.3 + 0.4 * 0.1 + 0.67 * 0.6 = 0.64 weighted_avg_f1−score=0.67∗0.3+0.4∗0.1+0.67∗0.6=0.64

如果处理不平衡数据时，但需要更多考虑算法在数据量较多的类别数据上表现效果，可以使用加权平均Weighted Average。

5.Micro Average

m i c r o _ f 1 _ s c o r e = a c c u r a c y = m i c r o _ p r e c i s i o n = m i c r o _ r e c a l l micro\_f1\_score = accuracy = micro\_precision = micro\_recall micro_f1_score=accuracy=micro_precision=micro_recall

其等同于accuracy列对应的f1-score。在处理平衡数据时可以考虑使用Micro Average.

参考资料

• Micro, Macro & Weighted Averages of F1 Score, Clearly Explained
• sklearn.metrics.classification_report

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