InformationValue (version 1.2.3)

misClassError: misClassError

Description

Calculate the percentage misclassification error for the given actuals and probaility scores.

Usage

misClassError(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.

Value

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

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.

Examples

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