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

Value

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

Author(s)

Arno De Caigny, a.de-caigny@ieseg.fr, Kristof Coussement, k.coussement@ieseg.fr and Koen W. De Bock, kdebock@audencia.com

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)

Runs v-fold cross validation with LLM

Description

In v-fold cross validation, the data are divided into v subsets of approximately equal size. Subsequently, one of the v data parts is excluded while the remaider of the data is used to create a logitleafmodel object. Predictions are generated for the excluded data part. The process is repeated v times.

Usage

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

Arguments

Value

An object of class llm.cv, which is a list with the following components:

Author(s)

Arno De Caigny, a.de-caigny@ieseg.fr, Kristof Coussement, k.coussement@ieseg.fr and Koen W. De Bock, kdebock@audencia.com

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

Examples

## Load PimaIndiansDiabetes dataset from mlbench package
if (requireNamespace("mlbench", quietly = TRUE)) {
  library("mlbench")
}
data("PimaIndiansDiabetes")
## Create the LLM with 5-cv
Pima.llm <- llm.cv(X = PimaIndiansDiabetes[,-c(9)],Y = PimaIndiansDiabetes$diabetes, cv=5,
 threshold_pruning = 0.25,nbr_obs_leaf = 100)

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 class 'llm'
predict(object, X, ...)

Arguments

Value

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

Author(s)

Arno De Caigny, a.de-caigny@ieseg.fr, Kristof Coussement, k.coussement@ieseg.fr and Koen W. De Bock, kdebock@audencia.com

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

## 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"])

Create the HTML code for Logit Leaf Model visualization

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

Value

Generates HTML code for a visualization.

Author(s)

Arno De Caigny, a.de-caigny@ieseg.fr, Kristof Coussement, k.coussement@ieseg.fr and Koen W. De Bock, kdebock@audencia.com

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")

Create the HTML code for Logit Leaf Model visualization

Description

This function generates HTML code for a visualization of the logit leaf model.

Usage

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

Arguments

Value

Generates HTML code for a visualization.

Author(s)

Arno De Caigny, a.de-caigny@ieseg.fr, Kristof Coussement, k.coussement@ieseg.fr and Koen W. De Bock, kdebock@audencia.com

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)
## 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")