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Сегодня есть три популярных метода бустинга, отличия которых хорошо донесены в статье CatBoost vs. LightGBM vs. XGBoost [скрыть все] [развернуть все] 6 комментариев “Reduced overfitting” which Yandex says helps you get better results in a training program. So that's awesome... The benchmarks at the bottom of https://catboost.yandex/ are somewhat useful though. I do remember when LightGBM came out and the benchmarks vs XGB were... very selective though. Jun 05, 2018 · Similar to random forests, except that instead of a variance-reducing bagging approach (multiple decision trees in a forest reduce possibility of a single tree overfitting the training dataset), gradient boosted trees utilize a boosting approach. Like bagging, boosting uses an ensemble of models (decision trees) to reduce variance, but unlike ... Catboost Metrics