The implementation of `gradient_boost.py:BinomialDeviance`?
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.
Does anyone can help me? Thanks. On Wed, Oct 26, 2016 at 10:42 AM, 颜发才(Yan Facai) <yafc18@gmail.com> wrote:
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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颜发才(Yan Facai)