topDecileLift: Calculate top-decile lift

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

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, [e-mail]

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)

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