Lessons Learned from Benchmarking Fast Machine Learning Algorithms

Re:Lessons Learned from Benchmarking Fast Machine Learning A... by shoo on Wednesday July 26, 2017 @06:20AM (#14854967)

from the catboost page there's also a link to this:

Fighting biases with dynamic boosting - Dorogush, Gulin, Gusev, Kazeev, Prokhorenkova, Vorobev

https://arxiv.org/pdf/1706.09516.pdf

> While gradient boosting algorithms are the workhorse of modern industrial machine learning and data science, all current implementations are susceptible to a non-trivial but damaging form of label leakage. It results in a systematic bias in pointwise gradient estimates that lead to reduced accuracy

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Re:Lessons Learned from Benchmarking Fast Machine Learning A... by cropsieboss on Wednesday July 26, 2017 @06:57AM (#14855128)

I was thinking of this one: https://arxiv.org/pdf/1706.01109.pdf

I see a github link in there https://github.com/arogozhnikov/infiniteboost, but it does not seem to be in CatBoost (as someone here pointed out better logloss has to do with CatBoost handling of categorical features).

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