misclassCost {CustomerScoringMetrics}  R Documentation 
Calculates the absolute misclassification cost value for a set of predictions.
misclassCost(predTest, depTest, costType = c("costRatio", "costMatrix", "costVector"), costs = NULL, cutoff = 0.5, dyn.cutoff = FALSE, predVal = NULL, depVal = NULL)
predTest 
Vector with predictions (realvalued 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 realvalued 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 (realvalued or discrete). Only used if

depVal 
Optional vector with true class labels for validation data. Only
used if 
A list with the following elements:
misclassCost 
Total misclassification cost value 
cutoff 
the threshold value used to convert realvalued predictions to class predictions 
Koen W. De Bock, kdebock@audencia.com
Witten, I.H., Frank, E. (2005): Data Mining: Practical Machine Learning Tools and Techniques, Second Edition. Chapter 5. Morgan Kauffman.
dynConfMatrix
,expMisclassCost
,dynAccuracy
## 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)