DualPredict {tools4uplift} | R Documentation |
Predictions from the two-model uplift model estimator with associated model performance.
DualPredict(data, treat, outcome, model, nb.group = 10, plotit = FALSE)
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 |
nb.group |
number of groups of equal observations in which to partition the data in order to compute model performance. |
plotit |
if |
data |
a data frame augmented with the predicted uplift |
qini |
a Qini Coefficient |
Mouloud Belbahri
Hansotia, B., J., and Rukstales B. (2001) Direct marketing for multichannel retailers: Issues, challenges and solutions. Journal of Database Marketing and Customer Strategy Management, Vol. 9(3), 259-266.
Belbahri, M., Murua, A., Gandouet, O., and Partovi Nia, V. (2019) Uplift Regression, <https://dms.umontreal.ca/~murua/research/UpliftRegression.pdf>
DualUplift
library(tools4uplift) data("SimUplift") fit <- DualUplift(SimUplift, "treat", "y", predictors = colnames(SimUplift[, 3:12])) pred <- DualPredict(SimUplift, "treat", "y", model = fit, nb.group = 5)[[1]]