目录
1. 使用sklearn计算accuracy（accuracy_score）、混淆矩阵（confusion_matrix）
2. 使用sklearn计算精确率precision、召回率recall、F1 score
参考：
https://blog.csdn.net/kan2281123066/article/details/103237273 代码：利用sklearn 计算 precision、recall、F1 score
https://blog.csdn.net/blythe0107/article/details/75003890 代码：sklearn的precision_score, recall_score, f1_score使用
https://blog.csdn.net/Urbanears/article/details/105033731 二分类和多分类问题下的评价指标详析（Precision, Recall, F1，Micro，Macro）
https://blog.csdn.net/wf592523813/article/details/95202448 二分类和多分类问题的评价指标总结
https://zhuanlan.zhihu.com/p/147663370 知乎：多分类模型Accuracy, Precision, Recall和F1-score的超级无敌深入探讨
https://zhuanlan.zhihu.com/p/59862986 知乎：详解sklearn的多分类模型评价指标
https://www.zhihu.com/question/51470349 知乎：对多分类数据的模型比较选择，应该参考什么指标？
0. 评估指标
- 精确度：precision，正确预测为正的，占全部预测为正的比例，TP / (TP+FP)
- 召回率：recall，正确预测为正的，占全部实际为正的比例，TP / (TP+FN)
- F1-score：精确率和召回率的调和平均数，2 * precision*recall / (precision+recall)
1. 使用sklearn计算accuracy（accuracy_score）、混淆矩阵（confusion_matrix）
举的这个例子，标签label为5类，是个多分类问题
- accuracy_score
- confusion_matrix
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score, recall_score, f1_score
actual = data3['label'] #真实的类别标签（data3是个dataframe，label是其中的一列）
predicted = data3['Label'] #预测的类别标签
计算总的精度
acc = accuracy_score(actual, predicted)
print(acc)
计算混淆矩阵
confusion_matrix(actual, predicted)
'''
输出结果：
0.8969907407407407
array([[1032, 1, 0, 0, 0],
[ 3, 965, 17, 0, 15],
[ 0, 10, 42, 0, 6],
[ 0, 11, 3, 0, 0],
[ 0, 98, 103, 0, 286]])
'''
2. 使用sklearn计算精确率precision、召回率recall、F1 score
- precision：precision_score https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html
- recall：recall_score https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html
- F1 score：f1_score https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html
参数 average有5个选项：{‘micro’微平均, ‘macro’宏平均, ‘samples’, ‘weighted’, ‘binary’}，默认是default=’binary’二分类
p = precision_score(actual, predicted, average='micro')
p2 = precision_score(actual, predicted, average='macro')
p3 = precision_score(actual, predicted, average='weighted')
#p4 = precision_score(actual, predicted, average='samples')
r = recall_score(actual, predicted, average='micro')
r2 = recall_score(actual, predicted, average='macro')
r3 = recall_score(actual, predicted, average='weighted')
f1score = f1_score(actual, predicted, average='micro')
f1score2 = f1_score(actual, predicted, average='macro')
f1score3 = f1_score(actual, predicted, average='weighted')
#f1score4 = f1_score(actual, predicted, average='samples')
print(p,p2,p3)
print(r,r2,r3)
print(f1score,f1score2,f1score3)
'''
输出结果：
0.8969907407407407 0.614528783337003 0.9212413779093402
0.8969907407407407 0.6550877741326565 0.8969907407407407
0.8969907407407407 0.6041619746930149 0.8986671343868164
'''
Original: https://blog.csdn.net/weixin_39450145/article/details/115284725
Author: weixin_39450145
Title: python：使用sklearn 计算 precision、recall、F1 score（多分类）
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