upliftRF: Uplift Random Forests

In uplift: Uplift Modeling

Description

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

Usage

## S3 method for class 'formula'
upliftRF(formula, data, ...)

## Default S3 method:
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,
...)  

## S3 method for class 'upliftRF'
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 <leaf> node.
minbucket_ct1	the minimum number of treatment observations in any terminal <leaf> 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.

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).

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

Author(s)

Leo Guelman <[e-mail]>

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)

Example output

Loading required package: RItools
Loading required package: SparseM

Attaching package: 'SparseM'

The following object is masked from 'package:base':

    backsolve

Loading required package: MASS
Loading required package: coin
Loading required package: survival
Loading required package: tables
Loading required package: Hmisc
Loading required package: lattice
Loading required package: Formula
Loading required package: ggplot2

Attaching package: 'Hmisc'

The following objects are masked from 'package:base':

    format.pval, round.POSIXt, trunc.POSIXt, units

Loading required package: penalized
Welcome to penalized. For extended examples, see vignette("penalized").
uplift: status messages enabled; set "verbose" to false to disable
upliftRF: starting. Tue Oct 10 21:07:16 2017 
10 out of 100 trees so far...
20 out of 100 trees so far...
30 out of 100 trees so far...
40 out of 100 trees so far...
50 out of 100 trees so far...
60 out of 100 trees so far...
70 out of 100 trees so far...
80 out of 100 trees so far...
90 out of 100 trees so far...
Call:
upliftRF(formula = y ~ X1 + X2 + X3 + X4 + X5 + X6 + trt(treat), 
    data = dd, mtry = 3, ntree = 100, split_method = "KL", minsplit = 200, 
    verbose = TRUE)

Uplift random forest
Number of trees: 100
No. of variables tried at each split: 3
Split method: KL
$call
upliftRF(formula = y ~ X1 + X2 + X3 + X4 + X5 + X6 + trt(treat), 
    data = dd, mtry = 3, ntree = 100, split_method = "KL", minsplit = 200, 
    verbose = TRUE)

$importance
  var  rel.imp
1  X1 39.97286
2  X2 25.07182
3  X4 18.37845
4  X3 16.57687

$ntree
[1] 100

$mtry
[1] 3

$split_method
[1] "KL"

attr(,"class")
[1] "summary.upliftRF"

uplift documentation built on May 2, 2019, 9:32 a.m.