Page 359 of Elements Of Statistical Learning 2nd edition says the below.

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Can someone explain the intuition & simplify it in layman terms?


  1. What is the reason/intuition & math behind fitting each successive tree in GBM on the negative gradient of the loss function?
  2. 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?

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