llm {LLM}

Create Logit Leaf Model

Description

This function creates the logit leaf model. It takes a dataframe with numeric values as input and a corresponding vector with dependent values. Decision tree parameters threshold for pruning and number of observations per leaf can be set.

Usage

llm(X, Y, threshold_pruning = 0.25, nbr_obs_leaf = 100)

Arguments

X

Dataframe containing numerical independent variables.

Y

Numerical vector of dependent variable. Currently only binary classification is supported.

threshold_pruning

Set confidence threshold for pruning. Default 0.25.

nbr_obs_leaf

The minimum number of observations in a leaf node. Default 100.

Value

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

Segment Rules

The decision rules that define segments. Use table.llm.html to visualize.

Coefficients

The segment specific logistic regression coefficients. Use table.llm.html to visualize.

Full decision tree for segmentation

The raw decision tree. Use table.llm.html to visualize.

Observations per segment

The raw decision tree. Use table.llm.html to visualize.

Incidence of dependent per segment

The raw decision tree. Use table.llm.html to visualize.

Author(s)

Arno De Caigny, [e-mail], Kristof Coussement, [e-mail] and Koen W. De Bock, [e-mail]

References

Arno De Caigny, Kristof Coussement, Koen W. De Bock, A New Hybrid Classification Algorithm for Customer Churn Prediction Based on Logistic Regression and Decision Trees, European Journal of Operational Research (2018), doi: 10.1016/j.ejor.2018.02.009.

See Also

predict.llm, table.llm.html, llm.cv

Examples

## Load PimaIndiansDiabetes dataset from mlbench package
if (requireNamespace("mlbench", quietly = TRUE)) {
  library("mlbench")
}
data("PimaIndiansDiabetes")
## Split in training and test (2/3 - 1/3)
idtrain <- c(sample(1:768,512))
PimaTrain <-PimaIndiansDiabetes[idtrain,]
Pimatest <-PimaIndiansDiabetes[-idtrain,]
## Create the LLM
Pima.llm <- llm(X = PimaTrain[,-c(9)],Y = PimaTrain$diabetes,
 threshold_pruning = 0.25,nbr_obs_leaf = 100)