AUROC: AUROC

In selva86/car2: Performance analysis and companion functions such as AUROC, IV, WOE calculation for binary classification models (logistic, discriminant etc)

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 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.

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)

selva86/car2 documentation built on May 29, 2019, 5:55 p.m.