randomforest variable importance: results of mlr3 is different from randomForest packages #976

slecee · 2023-11-29T02:31:12Z

slecee
Nov 29, 2023

i want to know why the results are different? Thanks for the help!

mlr3

library(mlr3)library(mlr3verse)
library(mlr3learners)
library(randomForest)

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

a = learners$train(tasks,row_ids = split$train)
a$model$importance

setosa  versicolor  virginica MeanDecreaseAccuracy MeanDecreaseGini
Petal.Length 0.318035851 0.266299298 0.30394373          0.291450470        29.166804
Petal.Width  0.343312702 0.285824160 0.24060252          0.287471876        29.279836
Sepal.Length 0.044516441 0.017425256 0.03575046          0.032833987         7.045327
Sepal.Width  0.007014524 0.009913653 0.00355260          0.006423498         1.783527

randomForest

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

 setosa versicolor   virginica MeanDecreaseAccuracy MeanDecreaseGini
Sepal.Length 0.028980891 0.01123579 0.041315479          0.027508393         6.729108
Sepal.Width  0.007498441 0.01019763 0.006658933          0.008441336         1.853704
Petal.Length 0.300187608 0.25654441 0.310950138          0.285067350        28.924225
Petal.Width  0.361163535 0.29720214 0.250040471          0.299030008        29.750160

actually,setting the seed does not yield the same results!

be-marc · 2023-11-29T08:39:52Z

be-marc
Nov 29, 2023
Maintainer Sponsor

I already answered your question #974.

1 reply

slecee Dec 1, 2023
Author

The result is still different...

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Uh oh!

Uh oh!

randomforest variable importance: results of mlr3 is different from randomForest packages #976

Uh oh!

{{title}}

Uh oh!

Replies: 1 comment 1 reply

Uh oh!

{{title}}

Uh oh!

Uh oh!

{{title}}

Uh oh!

Select a reply

Uh oh!

Uh oh!

randomforest variable importance: results of mlr3 is different from randomForest packages #976

Uh oh!

slecee Nov 29, 2023

Replies: 1 comment · 1 reply

Uh oh!

be-marc Nov 29, 2023 Maintainer Sponsor

Uh oh!

slecee Dec 1, 2023 Author

slecee
Nov 29, 2023

Replies: 1 comment 1 reply

be-marc
Nov 29, 2023
Maintainer Sponsor

slecee Dec 1, 2023
Author