Variable importance using the caret package (error); RandomForest algorithm
Solution 1
The importance scores can take a while to compute and train
won't automatically get randomForest
to create them. Add importance = TRUE
to the train
call and it should work.
Max
Solution 2
That is becouse the obtained from train()
object is not a pure Random Forest model, but a list of different objects (containing the final model itself as well as cross-validation results etc). You may see them with ls(model2)
. So to use the final model just call varImp(model2$finalModel)
.
Jakub Langr
Updated on June 10, 2022Comments
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Jakub Langr about 2 years
I am trying to obtain the variable importance of a rf model in any way. This is the approach I have tried so far, but alternate suggestions are very welcome.
I have trained a model in R:
require(caret) require(randomForest) myControl = trainControl(method='cv',number=5,repeats=2,returnResamp='none') model2 = train(increaseInAssessedLevel~., data=trainData, method = 'rf', trControl=myControl)
The dataset is fairly large, but the model runs fine. I can access its parts and run commands such as:
> model2[3] $results mtry RMSE Rsquared RMSESD RsquaredSD 1 2 0.1901304 0.3342449 0.004586902 0.05089500 2 61 0.1080164 0.6984240 0.006195397 0.04428158 3 120 0.1084201 0.6954841 0.007119253 0.04362755
But I get the following error:
> varImp(model2) Error in varImp[, "%IncMSE"] : subscript out of bounds
Apparently there is supposed to be a wrapper, but that does not seem to be the case: (cf:http://www.inside-r.org/packages/cran/caret/docs/varImp)
varImp.randomForest(model2) Error: could not find function "varImp.randomForest"
But this is particularly odd:
> traceback() No traceback available > sessionInfo() R version 3.0.1 (2013-05-16) Platform: x86_64-redhat-linux-gnu (64-bit) locale: [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C [3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8 [5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8 [7] LC_PAPER=C LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C attached base packages: [1] parallel stats graphics grDevices utils datasets methods [8] base other attached packages: [1] elasticnet_1.1 lars_1.2 klaR_0.6-9 MASS_7.3-26 [5] kernlab_0.9-18 nnet_7.3-6 randomForest_4.6-7 doMC_1.3.0 [9] iterators_1.0.6 caret_5.17-7 reshape2_1.2.2 plyr_1.8 [13] lattice_0.20-15 foreach_1.4.1 cluster_1.14.4 loaded via a namespace (and not attached): [1] codetools_0.2-8 compiler_3.0.1 grid_3.0.1 stringr_0.6.2 [5] tools_3.0.1
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Paul Lo almost 10 yearsThis doesn't work for me, I made it work by adding importance = TRUE.