In R, I ran multiple configurations. The weird thing is that increasing nodesize improves the accuracy quite a bit. This is the opposite of what I expected.
nodesize determines the minimal size of the final nodes (leafs). So a bigger number would mean a less deep tree used. Correct?
set.seed(23) Model4 <- train(Myformula4, method = "rf", data = Train, importance = T, nodesize=1, ntree=100, trControl = trainControl(method = "cv", number = 10)))
So this is searching for an optimal mtry (number of features used). Weird thing is I played around with nodesize (1 is advised for classification). Changed it to 50, 90, 100, 150 even 500. And the higher, the more accurate. Any ideas? Details: My data is 25 features, some of which correlated. 25000 rows of data.