Hello,

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?