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 :
Structure of the confusion matrix of a binary classifier and a dataset split in two classes
Each cell denotes respectively the number of true negatives (TN), false negatives (FN), false positives (FP) and true positives (TP).
The sensitivity measures among all 1-elements the probability that a classifier C has to 1-classify an element. It is equivalent to a measure called « recall » in the field of information retrieval.
Sensitivity or Recall Definition
It is opposed to the specificity, which measures among all 0-elements the probability for a classifier to 0-classify an element.
Specificity definition