I have been testing conditional trees and random forests with caret, and I've noticed it does something weird with factors.
So, for example, a ctree using the base dataset
# Via 'party' ctreeNC = ctree(weight ~ feed, data=chickwts) plot(ctreeNC, type="simple")
# Via 'caret' ctreeCARET <- train(weight ~ feed, data = chickwts, method = "ctree") plot(ctreeCARET$finalModel, type = "simple")
And with random forest:
# Via 'randomForest' rfNOCARET <- randomForest(weight ~ feed, data=chickwts) >importance(rfNOCARET) IncNodePurity feed 242589 # Via 'caret' rfCARET <- train(weight ~ feed, method = "rf", data = chickwts) >varImp(rfCARET) IncNodePurity feedhorsebean 120992.97 feedlinseed 50673.75 feedmeatmeal 15688.51 feedsoybean 32652.89 feedsunflower 23308.30
I read that train only accepted numerical values (in 2008), but I'm not sure if this has changed by now. Any insights?