Map 23 summary

One 、mAP Relevant concepts
1、 Positive and negative examples
Now let's assume that there are only two categories of classification goals , Count as a positive example （positive） And negative examples （negtive）, Then we can get the following four situations ：
（1）True positives(TP): The number of positive examples that are correctly classified , That is, the number of instances that are actually positive examples and are divided into positive examples by the classifier （ Sample size ）;
（2）False positives(FP): The number of cases wrongly divided into positive cases , That is, the number of instances that are actually negative but are classified as positive by the classifier ;
（3）False negatives(FN): The number of wrongly divided negative examples , That is, the number of instances that are actually positive but are classified as negative by the classifier ;
（4）True negatives(TN): The number of examples that are correctly divided into negative ones , That is, the number of instances that are actually negative and are divided into negative instances by the classifier .

2、P（ Accuracy ）
P representative precision, That is, accuracy , The accuracy rate represents the proportion of the actual positive samples in the predicted samples to all the positive samples , The formula is ：

Accuracy = Correctly predict the actual number of positive samples in the sample / All positive samples

namely precision = TP/（TP+FP）;

3、R（ Recall rate ）
R representative recall , Recall rate , Recall rate refers to the proportion of actual positive samples in all predicted samples , The formula is ：

Recall rate = Correctly predict the actual number of positive samples in the sample / Actual number of positive samples

namely Recall = TP/(TP+FN) ; Generally speaking , The higher the recall rate , The lower the accuracy .

4、ACC（ Accuracy rate ）
ACC representative Accuracy, That is, accuracy , The accuracy rate represents the proportion of the predicted correct number in all samples , The formula is ：

Accuracy rate = Predict the number of correctly classified samples in the sample / Number of all samples

namely ACC = （TP+TN）/（TP+FP+TN+FN）;

5、AP（ Average accuracy ）
AP representative Average Precision, Average accuracy .