I am going through Elements of statistical Learning and trying to understand GBM algorithm.
I understand general gradient descent algorithm mentioned below very well.
- Which parameter (theta j in the above picture) of GBM is gradient descent updating using each new tree that is added to GBM? Can you explain the above GBM algorithm intuitive in this context?
- What is the gamma in the GBM algorithm and intuition behind it?
- Seems gamma is calculated for each terminal region per each tree. What does it mean/do?
- GBM does not use reweighing of training samples unlike Adaboost which does. True or False?