Create Logit Leaf Model Prediction
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.
## S3 method for class 'llm' predict(object, X, ...)
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. |
Returns a dataframe containing a probablity for every instance based on the LLM model. Optional rownumbers can be added.
Arno De Caigny, a.de-caigny@ieseg.fr, Kristof Coussement, k.coussement@ieseg.fr and Koen W. De Bock, kdebock@audencia.com
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.
## 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"])Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.