dynAccuracy: Calculate accuracy

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

Description

Calculates accuracy (percentage correctly classified instances) for real-valued classifier predictions, with the optional ability to dynamically determine an incidence-based cutoff value using validation sample predictions

Usage

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

Arguments

predTest	Vector with predictions (real-valued or discrete)
depTest	Vector with real class labels
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.
cutoff	Threshold for converting real-valued predictions into class predictions. Default 0.5.
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.

Value

Accuracy value

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

Author(s)

Koen W. De Bock, [e-mail]

See Also

dynConfMatrix,confMatrixMetrics

Examples

## Load response modeling data set
data("response")
## Apply dynAccuracy function to obtain the accuracy that is achieved on the test sample.
## Use validation sample predictions to dynamically determine a cutoff value.
acc<-dynAccuracy(response$test[,2],response$test[,1],dyn.cutoff=TRUE,predVal=
response$val[,2],depVal=response$val[,1])
print(acc)

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