Hi,
which paper or book is the foundation of the implementation of
`gradient_boost.py:BinomialDeviance`?
I recently read the paper: Friedman: greedy function approximation - a
gradient boosting machine. I believe that L2_TreeBoost in the paper should
be equivalent to BinomialDeviance in scikit-learn, while their
implementation are different, for example:
+ negative_gradient:
- in scikit: \tilde{y} = y - expit(pred.ravel())
= y - \frac{1}{1 + exp(- F)}
- in paper: \tilde{y} = \frac{2 y}{1 + exp(2yF)}
Does anyone can help me?
Thanks.
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