misClassError: misClassError

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

Description

Calculate the percentage misclassification error for this logit model's fitted values.

Usage

misClassError(actuals, predictedScores, threshold = 0.5)

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.
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, misclassfication error is the number of mismatches between the predicted and actuals direction of the binary y variable.

Value

The misclassification error, which tells what proportion of predicted direction did not match with the actuals.

Author(s)

Selva Prabhakaran [e-mail]

Examples

data('ActualsAndScores')
misClassError(actuals=ActualsAndScores$Actuals,
  predictedScores=ActualsAndScores$PredictedScores, threshold=0.5)

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