In order to evaluate/compare the performance of classification algorithms, people tend to use *precision and recall*

Basically, here are the meanings:

- Precision: The bigger the better. It tries to measure how well the algorithm avoids false
**positive**. (i.e., the number of false positive is big or not). Or, it is the percentages of true positive which are correctly measured. Or, ratio of correctly true positive items in the true positive set. - Recall (i.e., sensitive): How well the algorithm tries avoiding the false
**negative**. (the number of false negative is big or not). Or, ratio of true positive according to the real/actually true positive set (training/test set)

Some preferences

2. Deep Learning – A practitioner’s approach, Josh & Adam, O’reilly, 2017

Update:

Another easy to grab explanation is from Apple documentation:

Precision and recall are actually two metrics. But they are often used together. Precision answers the question:

Out of the items that the classifier predicted to be true, how many are actually true?Whereas, recall answers the question:Out of all the items that are true, how many are found to be true by the classifier?