771: Gradient Boosting: XGBoost, LightGBM and CatBoost, with Kirill Eremenko
Show notes
Kirill Eremenko joins Jon Krohn for another exclusive, in-depth teaser for a new course just released on the SuperDataScience platform, “Machine Learning Level 2”. Kirill walks listeners through why decision trees and random forests are fruitful for businesses, and he offers hands-on walkthroughs for the three leading gradient-boosting algorithms today: XGBoost, LightGBM, and CatBoost. This episode is brought to you by Ready Tensor, where innovation meets reproducibility, and by Data Universe, the out-of-this-world data conference. Interested in sponsoring a SuperDataScience Podcast episode? Visit passionfroot.me/superdatascience for sponsorship information. In this episode you will learn: • All about decision trees [09:17] • All about ensemble models [21:43] • All about AdaBoost [36:47] • All about gradient boosting [45:52] • Gradient boosting for classification problems [59:54] • Advantages of XGBoost [1:03:51] • LightGBM [1:17:06] • CatBoost [1:32:07] Additional materials: www.superdatascience.com/771
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