AUROC: AUROC

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

Description

Calculate the area uder ROC curve statistic for a given logit model.

Usage

AUROC(actuals, predictedScores)

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.

Details

For a given actuals and predicted probability scores, the area under the ROC curve shows how well the model performs at capturing the false events and false non-events. An best case model will have an area of 1. However that would be unrealistic, so the closer the aROC to 1, the better is the model.

Value

The area under the ROC curve for a given logit model.

Author(s)

Selva Prabhakaran [e-mail]

Examples

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

Example output

[1] 0.8698962

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