VariableImportancePlot: Barplot comparing the feature importance across different...

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

Barplot comparing the feature importance across different learning methods.

Description

Barplot comparing the feature importance across different learning methods.

Usage

VariableImportancePlot(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 barplot that compares the standardized feature importance across 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)

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

VariableImportancePlot(RF = ensemblist$ModelObject$ranger,
GBM = ensemblist$ModelObject$gbm)

VariableImportancePlot(DT = ensemblist$ModelObject$rpart)

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