tools4uplift (version 0.1-0)

BestFeatures: Feature selection for the interaction estimator

Description

Penalized logistic regression (LASSO) in order to select the features that maximize the Qini coefficient.

Usage

BestFeatures(data, treat, outcome, predictors, nb.lambda = 100, nb.group = 10, 
              validation = FALSE, p = 0.3, value = 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).

predictors

a vector of names representing the predictors to consider in the model.

nb.lambda

the number of lambda values - Default is 100.

nb.group

the number of groups for computing the Qini coefficient - Default is 10.

validation

if TRUE, the best features are selected based on cross-validation - Default is FALSE.

p

if validation is TRUE, the desired proportion for the validation set. p is a value between 0 and 1 expressed as a decimal, it is set to be proportional to the number of observations per group - Default is 0.3.

value

if TRUE, the values of the best lambda and Qini coefficient will be printed - Default is FALSE.

Value

a vector of names representing the selected best features from the penalized logistic regression.

Details

The regularization parameter is chosen based on the interaction uplift model that maximizes the Qini coefficient. Using the LASSO penalty, some predictors have coefficients set to zero.

Examples

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

features <- BestFeatures(data = SimUplift, treat = "treat", outcome = "y", 
                         predictors = colnames(SimUplift[,3:7]))
features

# }