Calculate the sensitivity for a given logit model
sensitivity(actuals, predictedScores, threshold = 0.5)The sensitivity of the given binary response actuals and predicted probability scores, which is, the number of observations with the event AND predicted to have the event divided by the nummber of observations with the event.
The actual binary flags for the response variable. It can take a numeric vector containing values of either 1 or 0, where 1 represents the 'Good' or 'Events' while 0 represents 'Bad' or 'Non-Events'.
The prediction probability scores for each observation. If your classification model gives the 1/0 predcitions, convert it to a numeric vector of 1's and 0's.
If predicted value is above the threshold, it will be considered as an event (1), else it will be a non-event (0). Defaults to 0.5.
This function was obtained from the InformationValue R package (https://github.com/selva86/InformationValue).