In the following, we consider a dataset of elements split into two sets ‘0’ and ‘1’ : an element belonging to the set x in the dataset is written « x-element ». We consider a classifier C that classifies elements into the two classes ‘0’ and ‘1’. An element classified in the set x is written « x-classified element ».
The structure of the confusion matrix of a binary classifier C is as follows :
Each cell denotes respectively the number of true negatives (TN), false negatives (FN), false positives (FP) and true positives (TP).
The precision measures the probability that an element 1-classified by the classifier is correctly classified.
The recall measures the probability of a 1-element to be classified by the classifier in the class ‘1’.
It is equivalent to another measure called « sensitivity« .