R: using ranger with caret, tuneGrid argument

问题:

I'm using the caret package to analyse Random Forest models built using ranger. I can't figure out how to call the train function using the tuneGrid argument to tune the model parameters.

I think I'm calling the tuneGrid argument wrong, but can't figure out why it's wrong. Any help would be appreciated.

data(iris)

library(ranger)
model_ranger <- ranger(Species ~ ., data = iris, num.trees = 500, mtry = 4,
                       importance = 'impurity')


library(caret)

# my tuneGrid object:
tgrid <- expand.grid(
  num.trees = c(200, 500, 1000),
  mtry = 2:4
)

model_caret <- train(Species  ~ ., data = iris,
                     method = "ranger",
                     trControl = trainControl(method="cv", number = 5, verboseIter = T, classProbs = T),
                     tuneGrid = tgrid,
                     importance = 'impurity'
)

回答1:

Here is the syntax for ranger in caret:

library(caret)

add . prior to tuning parameters:

tgrid <- expand.grid(
  .mtry = 2:4,
  .splitrule = "gini",
  .min.node.size = c(10, 20)
)

Only these three are supported by caret and not the number of trees. In train you can specify num.trees and importance:

model_caret <- train(Species  ~ ., data = iris,
                     method = "ranger",
                     trControl = trainControl(method="cv", number = 5, verboseIter = T, classProbs = T),
                     tuneGrid = tgrid,
                     num.trees = 100,
                     importance = "permutation")

to get variable importance:

varImp(model_caret)

#output
             Overall
Petal.Length 100.0000
Petal.Width   84.4298
Sepal.Length   0.9855
Sepal.Width    0.0000

To check if this works set number of trees to 1000+ - the fit will be much slower. After changing importance = "impurity":

#output:

             Overall
Petal.Length  100.00
Petal.Width    81.67
Sepal.Length   16.19
Sepal.Width     0.00

If it does not work I recommend installing latest ranger from CRAN and caret from git hub:

devtools::install_github('topepo/caret/pkg/caret')

To train the number of trees you can use lapply with fixed folds created by createMultiFolds or createFolds.