tools4uplift (version 0.1-1)

InterPredict: Predictions from an interaction estimator

Description

Predictions from the interaction uplift model estimator with associated model performance.

Usage

InterPredict(data, treat, outcome, model, nb.group = 10, plotit = FALSE)

Arguments

data

a data frame containing the treatment, the outcome and the predictors.

treat

name of a binary (numeric) vector representing the treatment assignment (coded as 0/1).

outcome

name of a binary response (numeric) vector (coded as 0/1).

model

a model that must be the output of InterUplift function.

nb.group

number of groups of equal observations in which to partition the data in order to compute model performance.

plotit

if TRUE, a QiniCurve and QiniBarPlot are returned.

Value

data

a data frame augmented with the predicted uplift

qini

a Qini Coefficient

References

Lo, V., S., Y. (2002) The true lift model: a novel data mining approach to response modeling in database marketing. ACM SIGKDD Explorations Newsletter, Vol. 4(2), 78-86.

Belbahri, M., Murua, A., Gandouet, O., and Partovi Nia, V. (2019) Uplift Regression, <https://dms.umontreal.ca/~murua/research/UpliftRegression.pdf>

See Also

InterUplift

Examples

# NOT RUN {
library(tools4uplift)
data("SimUplift")

fit <- InterUplift(SimUplift, "treat", "y", colnames(SimUplift[, 3:12]))

pred <- InterPredict(SimUplift, "treat", "y", model = fit, nb.group = 5)[[1]]

# }