1.4k citations · 2.5k across the 3 of their papers we have counts for
3 papers
Which Tricks Are Important for Learning to Rank?
Ivan Lyzhin, Aleksei Ustimenko, Andrey Gulin +1
Nowadays, state-of-the-art learning-to-rank methods are based on gradient-boosted decision trees (GBDT). The most well-known algorithm is LambdaMART which was proposed more than a…
CatBoost: gradient boosting with categorical features support
Anna Veronika Dorogush, Vasily Ershov, Andrey Gulin
In this paper we present CatBoost, a new open-sourced gradient boosting library that successfully handles categorical features and outperforms existing publicly available implement…
CatBoost: unbiased boosting with categorical features
Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev +2
This paper presents the key algorithmic techniques behind CatBoost, a new gradient boosting toolkit. Their combination leads to CatBoost outperforming other publicly available boos…