sklearn.metrics模块实现了几个损失、得分和效用函数来衡量分类性能
TP、True Positive 真阳性：预测为正，实际为正
FP、False Positive 假阳性：预测为正，实际为负
FN、False Negative 假阴性：预测为负、实际为正
TN、True Negative 真阴性：预测为负、实际为负
....
F1-score：
是统计学中用来衡量二分类模型精确度的一种指标，用于测量不均衡数据的精度。它同时兼顾了分类模型的精确率和召回率。F1-score可以看作是模型精确率和召回率的一种加权平均，它的最大值是1，最小值是0。
在多分类问题中，如果要计算模型的F1-score，则有两种计算方式，分别为micro-F1和macro-F1，这两种计算方式在二分类中与F1-score的计算方式一样，所以在二分类问题中，计算micro-F1=macro-F1=F1-score，micro-F1和macro-F1都是多分类F1-score的两种计算方式；
micro-F1：
marco-F1：
#指标测试 from sklearn import metrics from sklearn.metrics import accuracy_score, precision_score, recall_score from sklearn.metrics import f1_score def Evaluate1(y_test,y_predic): print(‘accuracy:‘, metrics.accuracy_score(y_test, y_predict)) #预测准确率输出 print(‘macro_precision:‘,metrics.precision_score(y_test,y_predict,average=‘macro‘)) #预测宏平均精确率输出 print(‘micro_precision:‘, metrics.precision_score(y_test, y_predict, average=‘micro‘)) #预测微平均精确率输出 # print(‘weighted_precision:‘, metrics.precision_score(y_test, y_predict, average=‘weighted‘)) #预测加权平均精确率输出 print(‘macro_recall:‘,metrics.recall_score(y_test,y_predict,average=‘macro‘))#预测宏平均召回率输出 print(‘micro_recall:‘,metrics.recall_score(y_test,y_predict,average=‘micro‘))#预测微平均召回率输出 # print(‘weighted_recall:‘,metrics.recall_score(y_test,y_predict,average=‘weighted‘))#预测加权平均召回率输出 print(‘macro_f1:‘,metrics.f1_score(y_test,y_predict,labels=[0,1,2,3,4,5,6],average=‘macro‘))#预测宏平均f1-score输出 print(‘micro_f1:‘,metrics.f1_score(y_test,y_predict,labels=[0,1,2,3,4,5,6,7],average=‘micro‘))#预测微平均f1-score输出 # print(‘weighted_f1:‘,metrics.f1_score(y_test,y_predict,labels=[0,1,2,3,4,5,6],average=‘weighted‘))#预测加权平均f1-score输出 #target_names = [‘class 1‘, ‘class 2‘, ‘class 3‘,‘class 4‘,‘class 5‘,‘class 6‘,‘class 7‘] # print(‘混淆矩阵输出:\n‘,metrics.confusion_matrix(y_test,y_predict,labels=[0,1,2,3,4,5,6]))#混淆矩阵输出 #比如[1,3]为2，即1类预测为3类的个数为2 # print(‘分类报告:\n‘, metrics.classification_report(y_test, y_predict,labels=[0,1,2,3,4,5,6]))#分类报告输出 ,target_names=target_names def Evaluate2(y_true,y_pred): print("accuracy:", accuracy_score(y_true, y_pred)) # Return the number of correctly classified samples print("macro_precision", precision_score(y_true, y_pred, average=‘macro‘)) print("micro_precision", precision_score(y_true, y_pred, average=‘micro‘)) # Calculate recall score print("macro_recall", recall_score(y_true, y_pred, average=‘macro‘)) print("micro_recall", recall_score(y_true, y_pred, average=‘micro‘)) # Calculate f1 score print("macro_f", f1_score(y_true, y_pred, average=‘macro‘)) print("micro_f", f1_score(y_true, y_pred, average=‘micro‘)) y_test = [1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 4, 4,5,5,6,6,6,0,0,0,0] y_predict = [1, 1, 1, 3, 3, 2, 2, 3, 3, 3, 4, 3, 4, 3,5,1,3,6,6,1,1,0,6] Evaluate1(y_test,y_predict) Evaluate2(y_test,y_predict) ##其中列表左边的一列为分类的标签名，右边support列为每个标签的出现次数．avg / total行为各列的均值（support列为总和）． ##precision recall f1-score三列分别为各个类别的精确度/召回率及 F1值 ‘‘‘ accuracy: 0.5217391304347826 macro_precision: 0.7023809523809524 micro_precision: 0.5217391304347826 macro_recall: 0.5261904761904762 micro_recall: 0.5217391304347826 macro_f1: 0.5441558441558441 micro_f1: 0.5217391304347826 accuracy: 0.5217391304347826 macro_precision 0.7023809523809524 micro_precision 0.5217391304347826 macro_recall 0.5261904761904762 micro_recall 0.5217391304347826 macro_f 0.5441558441558441 micro_f 0.5217391304347826 ‘‘‘
https://blog.csdn.net/lyb3b3b/article/details/84819931
https://blog.csdn.net/qq_43190189/article/details/105778058
原文：https://www.cnblogs.com/xuechengmeigui/p/13657545.html