sensitivity: sensitivity

In InformationValue: Performance Analysis and Companion Functions for Binary Classification Models

Description

Calculate the sensitivity for a given logit model.

Usage

sensitivity(actuals, predictedScores, threshold = 0.5)

Arguments

actuals	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'.
predictedScores	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.
threshold	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.

Details

For a given binary response actuals and predicted probability scores, sensitivity is defined as number of observations with the event AND predicted to have the event divided by the number of observations with the event. It can be used as an indicator to gauge how sensitive is your model in detecting the occurence of events, especially when you are not so concerned about predicting the non-events as true.

Value

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.

Author(s)

Selva Prabhakaran [e-mail]

Examples

data('ActualsAndScores')
sensitivity(actuals=ActualsAndScores$Actuals, predictedScores=ActualsAndScores$PredictedScores)

Example output

[1] 1

InformationValue documentation built on May 1, 2019, 9:12 p.m.