Recall (recall rate); Precision (accuracy rate); F1-meature (Comprehensive evaluation indicator); True Positives;false Positives;false negatives.

Recall (recall rate); Precision (accuracy rate); F1-meature (Comprehensive evaluation index); These parameters are often used in information retrieval (such as search engines), natural language processing, and detection classifications.

Precision: The percentage of information that is detected that is correct or relevant ( that is, what you want)(Number of positive samples predicted as a proportion of the total sample) ; Recall: The proportion of all correct information or related information (wanted) detected(Proportion of positive samples in total samples to the total number of predicted samples) .   the translation in the table is more important and can help to understand. True Positives (Na-True) false positives (Na pseudo) = false positives negatives (de-true) = False negatives True negatives (de-pseudo) where false positives (Na pseudo)Also commonly referred to as False positives , negativesAlso commonly referred to as false Negatives! Precision = tp/(TP + FP);Recall = TP/(TP + fn).There are also two other definitions:
     in practice, however, we certainly hope that the higher the result of the search, the better, the higher the r, the better; in fact, the two are contradictory in some cases. For example, we only search for a result, and it is accurate, then P is 100%, but R is very low, and if we return all the results, then the R is 100%, but p is very low. Therefore, in different situations, it is necessary to judge whether the P is higher or r is higher. If you are doing experiments, you can draw Precision-recall curves to help with the analysis. F-measure is a weighted harmonic average of precision and recall:

When the parameter is a=1, it is the most common F1:

It is easy to understand that F1 synthesized the results of P and R, when the F1 is higher, the experimental method is more ideal.

Recall (recall rate); Precision (accuracy rate); F1-meature (Comprehensive evaluation indicator); True Positives;false Positives;false negatives.