why mlr3 randomforest importance is different from randomForest package

[slecee]

why mlr3 randomforest importance is different from randomForest package

[be-marc]

You call partition() after setting the seed. This function already uses your seed to sample random splits. The importance values are equal when you move the partition() call.

library(mlr3extralearners)

tasks = as_task_classif(iris, target = 'Species')
learners = lrn("classif.randomForest" ,predict_type = "prob",importance= c('gini'))
split = partition(tasks)
split
set.seed(123, kind = "Mersenne-Twister")
a = learners$train(tasks,row_ids = split$train)
a$model$importance

library(randomForest)

set.seed(123, kind = "Mersenne-Twister")
tmp <- randomForest(iris[split$train,1:4], 
                    iris$Species[split$train], 
                    importance = TRUE)

tmp[["importance"]]

[slecee]

the results also differ...