| topDecileLift {CustomerScoringMetrics} | R Documentation |
Calculate top-decile lift
Description
Calculates top-decile lift, a metric that expresses how the incidence in the 10% customers with the highest model predictions compares to the overall sample incidence. A top-decile lift of 1 is expected for a random model. A top-decile lift of 3 indicates that in the 10% highest predictions, 3 times more postive cases are identified by the model than would be expected for a random selection of instances. The upper boundary of the metric depends on the sample incidence and is given by 100% / Indidence %. E.g. when the incidence is 10%, top-decile lift can be no higher than 10.
Usage
topDecileLift(predTest, depTest)
Arguments
predTest |
Vector with predictions (real-valued or discrete) |
depTest |
Vector with true class labels |
Value
Top-decile lift value
Author(s)
Koen W. De Bock, kdebock@audencia.com
References
Berry, M.J.A. and Linoff, G.S. (2004): "Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management - Second Edition". John Wiley & Sons.
See Also
liftTable, liftIndex, liftChart
Examples
## Load response modeling predictions
data("response")
## Calculate top-decile lift for test sample results
tdl<-topDecileLift(response$test[,2],response$test[,1])
print(tdl)