dynConfMatrix {CustomerScoringMetrics}

Calculate a confusion matrix

Description

Calculates a confusion matrix for real-valued classifier predictions, with the optional ability to dynamically determine an incidence-based cutoff value using validation sample predictions

Usage

dynConfMatrix(predTest, depTest, cutoff = 0.5, dyn.cutoff = FALSE,
  predVal = NULL, depVal = NULL, returnClassPreds = FALSE)

Arguments

predTest

Vector with predictions (real-valued or discrete)

depTest

Vector with real class labels

cutoff

Threshold for converting real-valued predictions into class predictions. Default 0.5.

dyn.cutoff

Logical indicator to enable dynamic threshold determination using validation sample predictions. In this case, the function determines, using validation data, the indidicence (occurrence percentage of the customer behavior or characterstic of interest) and chooses a cutoff value so that the number of predicted positives is equal to the number of true positives. If TRUE, then the value for the cutoff parameter is ignored.

predVal

Vector with predictions (real-valued or discrete). Only used if dyn.cutoff is TRUE.

depVal

Optional vector with true class labels for validation data. Only used if dyn.cutoff is TRUE.

returnClassPreds

Boolean value: should class predictions (using cutoff) be returned?

Value

A list with two elements:

confMatrix

a confusion matrix

cutoff

the threshold value used to convert real-valued predictions to class predictions

classPreds

class predictions, if requested using returnClassPreds

Author(s)

Koen W. De Bock, [e-mail]

References

Witten, I.H., Frank, E. (2005): Data Mining: Practical Machine Learning Tools and Techniques, Second Edition. Chapter 5. Morgan Kauffman.

See Also

dynAccuracy, confMatrixMetrics

Examples

## Load response modeling data set
data("response")
## Apply dynConfMatrix function to obtain a confusion matrix. Use validation sample
## predictions to dynamically determine an incidence-based cutoff value.
cm<-dynConfMatrix(response$test[,2],response$test[,1],dyn.cutoff=TRUE,
predVal=response$val[,2],depVal=response$val[,1])
print(cm)