confusionMatrix: confusionMatrix

Description Usage Arguments Details Value Author(s) Examples

Description

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

Usage

1
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 selva86@gmail.com

Examples

1
2
data('ActualsAndScores')
confusionMatrix(actuals=ActualsAndScores$Actuals, predictedScores=ActualsAndScores$PredictedScores)

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