misClassError: misClassError

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

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.

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)

Example output

[1] 0.4294

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