In machine learning, Sensitivity is used for validating the accuracy of a classifier, such as Logistic, Random Forest, and SVM. It is also known as REC (recall) or TPR (true positive rate). Sensitivity can be defined as the ratio of predicted true events and total events i.e.: Sensitivity = True Positives / Positives in Actual Dependent Variable Here, true events are those events that were true as predicted by a machine learning model. The best sensitivity is 1.0 and the worst sensitivity is 0.0.