Using RFE, you can get a importance rank of the features, but right now I can only use the model and parameter inner the package like: lmFuncs(linear model),rfFuncs(random forest)
it seems that
caretFuncs
can do some custom settings for your own model and parameter,but I don't know the details and the formal document didn't give detail, I want to apply svm and gbm to this RFE process,because this is the current model I used to train, anyone has any idea?
I tried to recreate working example based on the documentation. You correctly identified use of caretFuncs, you can then set your model parameters in rfe call (you can also define trainControl object etc).
# load caret
library(caret)
# load data, get target and feature column labels
data(iris)
col_names = names(iris);target = "Species"
feature_names = col_names[col_names!=target]
# construct rfeControl object
rfe_control = rfeControl(functions = caretFuncs, #caretFuncs here
method="cv",
number=5)
# construct trainControl object for your train method
fit_control = trainControl(classProbs=T,
search="random")
# get results
rfe_fit = rfe(iris[,feature_names], iris[,target],
sizes = 1:4,
rfeControl = rfe_control,
method="svmLinear",
# additional arguments to train method here
trControl=fit_control)
If you want to dive deeper into the matter you might want to visit links below.
rfe documentation with basic code snippets:
https://www.rdocumentation.org/packages/caret/versions/6.0-80/topics/rfe
caret documentation on rfe:
https://topepo.github.io/caret/recursive-feature-elimination.html
Hope this helps!
