misclassCost {CustomerScoringMetrics} | R Documentation |
Calculate misclassification cost
Description
Calculates the absolute misclassification cost value for a set of predictions.
Usage
misclassCost(predTest, depTest, costType = c("costRatio", "costMatrix",
"costVector"), costs = NULL, cutoff = 0.5, dyn.cutoff = FALSE,
predVal = NULL, depVal = NULL)
Arguments
predTest |
Vector with predictions (real-valued or discrete) |
depTest |
Vector with real class labels |
costType |
An argument that specifies how the cost information is provided. This
should be either |
costs |
see |
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 |
predVal |
Vector with predictions (real-valued or discrete). Only used if
|
depVal |
Optional vector with true class labels for validation data. Only
used if |
Value
A list with the following elements:
misclassCost |
Total misclassification cost value |
cutoff |
the threshold value used to convert real-valued predictions to class predictions |
Author(s)
Koen W. De Bock, kdebock@audencia.com
References
Witten, I.H., Frank, E. (2005): Data Mining: Practical Machine Learning Tools and Techniques, Second Edition. Chapter 5. Morgan Kauffman.
See Also
dynConfMatrix
,expMisclassCost
,dynAccuracy
Examples
## Load response modeling data set
data("response")
## Generate cost vector
costs <- runif(nrow(response$test), 1, 100)
## Apply misclassCost function to obtain the misclassification cost for the
## predictions for test sample. Assume a cost ratio of 5.
emc<-misclassCost(response$test[,2],response$test[,1],costType="costVector", costs=costs)
print(emc$EMC)