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I trained a decision tree classifier by means the package caret, This is the code:

library(caret)
trctrl <- trainControl(method = "repeatedcv", number = 10, repeats = 20,
                       search = "grid")
classifier = train(form = Target ~ ., data = training_set, method = 'rpart',
                   parms = list(split = "information"),trControl=trctrl,tuneLength = 10)

Since I would like to estimate the fatures importance, I executed:

importance <- varImp(classifier, scale=FALSE)

where str(importance) returns:

List of 3
 $ importance:'data.frame': 13 obs. of  1 variable:
  ..$ Overall: num [1:13] 234.1 409.4 46.5 22.8 618 ...
 $ model     : chr "rpart"
 $ calledFrom: chr "varImp"
 - attr(*, "class")= chr "varImp.train"

However, I don't understand how this command works. How is importance calculated?

Mark
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  • 6
  • take a look at [this answer](https://stats.stackexchange.com/questions/6478/how-to-measure-rank-variable-importance-when-using-cart-specifically-using) – Elia Apr 23 '21 at 12:15

0 Answers0