Page 359 of Elements Of Statistical Learning 2nd edition says the below.
Can someone explain the intuition & simplify it in layman terms?
- What is the reason/intuition & math behind fitting each successive tree in GBM on the negative gradient of the loss function?
- Is it done to make GBM more generalization on unseen test dataset? If so how does fitting on negative gradient achieve this generalization on test data?