table.cat.llm.html: Create the HTML code for Logit Leaf Model visualization

In LLM: Logit Leaf Model Classifier for Binary Classification

Description

This function generates HTML code for a visualization of the logit leaf model based on the variable importance per variable category.

Usage

table.cat.llm.html(
  object,
  category_var_df,
  headertext = "The Logit Leaf Model",
  footertext = "A table footer comment",
  roundingnumbers = 2,
  methodvarimp = "Coef"
)

Arguments

object	An object of class logitleafmodel, as that created by the function llm.
category_var_df	dataframe containing a column called "iv" with the independent variables and a column called "cat" with the variable category names that is associated with every iv
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.
methodvarimp	Allows to determine the method to calculate the variable importance. There are 4 options: 1/ Variable coefficent (method = 'Coef) 2/ Standardized beta ('Beta') 3/ Wald statistic ('Wald') 4/ Likelihood Rate Test ('LRT')

Value

Generates HTML code for a visualization.

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, llm, 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)
## Define the variable categories (note: the categories are only created for demonstration)
var_cat_df <- as.data.frame(cbind(names(PimaTrain[,-c(9)]),
c("cat_a","cat_a","cat_a","cat_a","cat_b","cat_b","cat_b","cat_b")), stringsAsFactors = FALSE)
names(var_cat_df) <- c("iv", "cat")
## Save the output of the model to a html file
Pima.Viz <- table.cat.llm.html(object = Pima.llm,category_var_df= var_cat_df,
 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")

LLM documentation built on July 1, 2020, 7:19 p.m.