confusionMatrix: confusionMatrix

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

Description

Calculate the confusion matrix for the fitted values for a logistic regression model.

Usage

confusionMatrix(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 actuals and predicted probability scores, the confusion matrix showing the count of predicted events and non-events against actual events and non events.

Value

For a given actuals and predicted probability scores, returns the confusion matrix showing the count of predicted events and non-events against actual events and non events.

Author(s)

Selva Prabhakaran [e-mail]

Examples

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

Example output

   0  1
0 12  0
1 73 85

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