LLM (version 1.1.0)

predict.llm: Create Logit Leaf Model Prediction

Description

This function creates a prediction for an object of class logitleafmodel. It assumes a dataframe with numeric values as input and an object of class logitleafmodel, which is the result of the llm function. Currently only binary classification is supported.

Usage

# S3 method for llm
predict(object, X, ...)

Arguments

object

An object of class logitleafmodel, as that created by the function llm.

X

Dataframe containing numerical independent variables.

...

further arguments passed to or from other methods.

Value

Returns a dataframe containing a probablity for every instance based on the LLM model. Optional rownumbers can be added.

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

llm, table.llm.html, llm.cv

Examples

# NOT RUN {
## 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)
## Use the model on the test dataset to make a prediction
PimaPrediction <- predict.llm(object = Pima.llm, X = Pimatest[,-c(9)])
## Optionally add the dependent to calculate performance statistics such as AUC
# PimaPrediction <- cbind(PimaPrediction, "diabetes" = Pimatest[,"diabetes"])
# }