uplift (version 0.3.4)

qini: Computes the Qini Coefficient Q

Description

This function computes the Qini coefficient from a performance object (as created by the function performance).

Usage

## S3 method for class 'performance':
qini(x, plotit = TRUE, ...)

Arguments

x

an object of class performance.

plotit

plot the incremental gains from the fitted model?

...

additional arguments passed to plot.

Value

• A list with the following components
• Qini
  the Qini coefficient as defined above.
• inc.gains
  the incremental gain values from the fitted model.
• random.inc.gains
  the random incremental gains.

Details

Qini coefficients represent a natural generalizations of the Gini coefficient to the case of uplift. Qini is defined as the area between the actual incremental gains curve from the fitted model and the area under the diagonal corresponding to random targeting. See the references for details.

References

Radcliffe, N. and Surry, P. (2011). Real-World Uplift Modelling with Significance-Based Uplift Trees. Portrait Technical Report, TR-2011-1.

Radcliffe, N. (2007). Using control groups to target on predicted lift: Building and assessing uplift models. Direct Marketing Analytics Journal, An Annual Publication from the Direct Marketing Association Analytics Council, pages 14-21.

Examples

library(uplift)

### simulate data for uplift modeling

set.seed(123)
dd <- sim_pte(n = 1000, p = 20, rho = 0, sigma =  sqrt(2), beta.den = 4)
dd$treat <- ifelse(dd$treat == 1, 1, 0) 

### fit uplift random forest

fit1 <- upliftRF(y ~ X1 + X2 + X3 + X4 + X5 + X6 + trt(treat),
                 data = dd, 
                 mtry = 3,
                 ntree = 100, 
                 split_method = "KL",
                 minsplit = 200, # need small trees as there is strong uplift effects in the data
                 verbose = TRUE)
print(fit1)
summary(fit1)

### predict on new data 

dd_new <- sim_pte(n = 2000, p = 20, rho = 0, sigma =  sqrt(2), beta.den = 4)
dd_new$treat <- ifelse(dd_new$treat == 1, 1, 0)  

pred <- predict(fit1, dd_new)

### evaluate model performance

perf <- performance(pred[, 1], pred[, 2], dd_new$y, dd_new$treat, direction = 1)

### compute Qini coefficient

Q <- qini(perf, plotit = TRUE)
Q