InterPredict {tools4uplift} | R Documentation |
Predictions from the interaction uplift model estimator with associated model performance.
InterPredict(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
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>
InterUplift
library(tools4uplift) data("SimUplift") fit <- InterUplift(SimUplift, "treat", "y", colnames(SimUplift[, 3:12])) pred <- InterPredict(SimUplift, "treat", "y", model = fit, nb.group = 5)[[1]]