View source: R/VariableImportanceTable.R
VariableImportanceTable | R Documentation |
Table comparing the feature importance for tree-based learning methods.
VariableImportanceTable(DT = NULL, RF = NULL, GBM = NULL)
DT |
A fitted decision tree model object |
RF |
A fitted random forest model object |
GBM |
A fitted gradient boosting model object |
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.
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)
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