dynConfMatrix: Calculate a confusion matrix

In CustomerScoringMetrics: Evaluation Metrics for Customer Scoring Models Depending on Binary Classifiers

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)

CustomerScoringMetrics documentation built on May 2, 2019, 5:17 a.m.