dynAccuracy {CustomerScoringMetrics} R Documentation

Calculate accuracy

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, kdebock@audencia.com

`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)

```

[Package CustomerScoringMetrics version 1.0.0 Index]