Exercise

# Early stopping in GBMs

Use the `gbm.perf()`

function to estimate the optimal number of boosting iterations (aka `n.trees`

) for a GBM model object using both OOB and CV error. When you set out to train a large number of trees in a GBM (such as 10,000) and you use a validation method to determine an earlier (smaller) number of trees, then that's called "early stopping". The term "early stopping" is not unique to GBMs, but can describe auto-tuning the number of iterations in an iterative learning algorithm.

Instructions

**100 XP**

- The
`credit_model`

object is loaded in the workspace. - Use the
`gbm.perf()`

function with the "OOB" method to get the optimal number of trees based on the OOB error and store that number as`ntree_opt_oob`

. - Train a new GBM model, this time with cross-validation, so we can get a cross-validated estimate of the optimal number of trees.
- Lastly, use the
`gbm.perf()`

function with the "cv" method to get the optimal number of trees based on the CV error and store that number as`ntree_opt_cv`

. - Compare the two numbers.