uplift (version 0.3.5)

upliftRF: Uplift Random Forests

Description

upliftRF implements Random Forests with split criteria designed for binary uplift modeling tasks.

Usage

"upliftRF"(formula, data, ...)
"upliftRF"(
x,  
y,  
ct, 
mtry = floor(sqrt(ncol(x))),
ntree = 100, 
split_method = c("ED", "Chisq", "KL", "L1", "Int"),
interaction.depth = NULL,
bag.fraction = 0.5,
minsplit = 20, 
minbucket_ct0 = round(minsplit/4), 
minbucket_ct1 = round(minsplit/4), 
keep.inbag = FALSE,
verbose = FALSE,
...)  
"print"(x, ...)

Arguments

data

A data frame containing the variables in the model. It should include a variable reflecting the binary treatment assignment of each observation (coded as 0/1).

x, formula

a data frame of predictors or a formula describing the model to be fitted. A special term of the form trt() must be used in the model equation to identify the binary treatment variable. For example, if the treatment is represented by a variable named treat, then the right hand side of the formula must include the term +trt(treat).

y

a binary response (numeric) vector.

ct

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

mtry

the number of variables to be tested in each node; the default is floor(sqrt(ncol(x))).

ntree

the number of trees to generate in the forest; default is ntree = 100.

split_method

the split criteria used at each node of each tree; Possible values are: "ED" (Euclidean distance), "Chisq" (Chi-squared divergence), "KL" (Kullback-Leibler divergence), "Int" (Interaction method).

interaction.depth

The maximum depth of variable interactions. 1 implies an additive model, 2 implies a model with up to 2-way interactions, etc. The default is to grow trees to maximal depth, constrained on the arguments specified in minsplit and minbucket.

bag.fraction

the fraction of the training set observations randomly selected for the purpose of fitting each tree in the forest.

minsplit

the minimum number of observations that must exist in a node in order for a split to be attempted.

minbucket_ct0

the minimum number of control observations in any terminal node.

minbucket_ct1

the minimum number of treatment observations in any terminal node.

keep.inbag

if set to TRUE, an nrow(x) by ntree matrix is returned, whose entries are the "in-bag" samples in each tree.

verbose

print status messages?

...

optional parameters to be passed to the low level function upliftRF.default.

Value

An object of class upliftRF, which is a list with the following components:

call

the original call to upliftRF

trees

the tree structure that was learned

split_method

the split criteria used at each node of each tree

ntree

the number of trees used

mtry

the number of variables tested at each node

var.names

a character vector with the name of the predictors

var.class

a character vector containing the class of each predictor variable

inbag

an nrow(x) by ntree matrix showing the in-bag samples used by each tree

Details

Uplift Random Forests estimate personalized treatment effects (a.k.a. uplift) by binary recursive partitioning. The algorithm and split methods are described in Guelman et al. (2013a, 2013b).

References

Guelman, L., Guillen, M., and Perez-Marin A.M. (2013a). Uplift random forests. Cybernetics & Systems, forthcoming.

Guelman, L., Guillen, M., and Perez-Marin A.M. (2013b). Optimal personalized treatment rules for marketing interventions: A review of methods, a new proposal, and an insurance case study. Submitted.

Su, X., Tsai, C., Wang, H., Nickerson, D., and Li, B. (2009). Subgroup Analysis via Recursive Partitioning. Journal of Machine Learning Research, 10, 141-158.

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, 
                 verbose = TRUE)
print(fit1)
summary(fit1)