VariableImportanceTable: Table comparing the feature importance for tree-based...

In LOGANTree: Tree-Based Models for the Analysis of Log Files from Computer-Based Assessments

Table comparing the feature importance for tree-based learning methods.

Description

Table comparing the feature importance for tree-based learning methods.

Usage

VariableImportanceTable(DT = NULL, RF = NULL, GBM = NULL)

Arguments

DT	A fitted decision tree model object
RF	A fitted random forest model object
GBM	A fitted gradient boosting model object

Value

This function returns a data frame that compares the feature importance from different tree-based machine learning methods. These measures are computed via the caret package.

Examples

library(gbm)
colnames(training)[14] <- "perf"
ensemblist <- TreeModels(traindata = training,
methodlist = c("dt", "rf","gbm"),checkprogress = TRUE)

VariableImportanceTable(DT = ensemblist$ModelObject$rpart,
RF = ensemblist$ModelObject$ranger,GBM = ensemblist$ModelObject$gbm)

VariableImportanceTable(DT = ensemblist$ModelObject$rpart,
RF = ensemblist$ModelObject$ranger)

VariableImportanceTable(DT = ensemblist$ModelObject$rpart)

LOGANTree documentation built on June 23, 2022, 1:06 a.m.