liftTable: Calculate lift table

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

Description

Calculates a lift table, showing for different percentiles of predicted scores how much more the characteristic or action of interest occurs than for the overall sample.

Usage

liftTable(predTest, depTest, resolution = 1/10)

Arguments

predTest	Vector with predictions (real-valued or discrete)
depTest	Vector with true class labels
resolution	Value for the determination of percentile intervals. Default 1/10 (10%).

Value

A lift table.

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

topDecileLift, liftIndex, liftChart

Examples

## Load response modeling predictions
data("response")
## Apply liftTable function to obtain lift table for test sample results and print
## results
lt<-liftTable(response$test[,2],response$test[,1])
print(lt)

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