I am trying to use gbm for a classification problem,but there is a part I do not understand:
churn.gbm <- gbm(formula = Churned ~ ., distribution = "bernoulli",data = logit.data, n.trees = 5000,interaction.depth = 3, **shrinkage** = 0.001,cv.folds = 4,verbose = T)
What does the shrinkage option do in gbm?
Someplaces I have looked at say that smaller shrinkage gives better results at the expense of more iterations,but then how do we determine an appropriate value of it?
When I have run this model and tried to generate the roc it says not enough distinct predictions,I have tried for shrinkage 0.01-0.001 but the results are all the same.
Can someone please help me in understanding this parameter?