Hello
Questions for Boosted Trees under Predictive Modeling platform:
1- What does an "estimate" represent at the split end mathematically ? Is it an expected change in response per unit change in the parameter (ie.. sensitivity ) ?
2- Assuming e.g. 200 layers of boosted trees model , is the final predicted model equal to the 200th i.e. last layers' model or an average of all 200 models ? I am not too sure which one as a boosted tree learns from the residuals from the prior one .. so I am inclined to think that perhaps it is the last model but ensemble methods are aggerated methods (counter argument).
3- Is there a practical method of estimating confidence intervals in a boosted tree model ? Perhaps, getting each layers' result to then create a distribution ?
Appreciate the support. thanks.
