Create the HTML code for Logit Leaf Model visualization
This function generates HTML code for a visualization of the logit leaf model.
table.llm.html( object, headertext = "The Logit Leaf Model", footertext = "A table footer comment", roundingnumbers = 2 )
object |
An object of class logitleafmodel, as that created by the function llm. |
headertext |
Allows to provide the table with a header. |
footertext |
Allows to provide the table with a custom footer. |
roundingnumbers |
An integer stating the number of decimals in the visualization. |
Generates HTML code for a visualization.
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)
## Save the output of the model to a html file
Pima.Viz <- table.llm.html(object = Pima.llm, headertext = "This is an example of the LLM model",
footertext = "Enjoy the package!")
## Optionaly write it to your working directory
# write(Pima.Viz, "Visualization_LLM_on_PimaIndiansDiabetes.html")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.