GBMmodel = gbm(mydataset~x1+x2+x3+x4+x5,
data=mydataset,distribution="gaussian",n.trees=1500,shrinkage=0.005,interaction.depth=3, bag.fraction=0.75,train.fraction=0.75,n.minobsinnode=5,cv.folds=3,keep.data=TRUE,verbose=TRUE)
Predicts = predict.gbm(...)
Then, we can obtain the "Predicts". I make R2 between mydataset and Predicts. I see, in this case, interaction.depth=3, R2 is about 0.7; if we set interaction.depth=5, R2 is about 0.8. So, how to specify interaction depth? It seems that interaction.depth is more, the fitted result is better. Why? And which interaction depth should be specified in GBM? choose 10? 20? ...