771: Gradient Boosting: XGBoost, LightGBM and CatBoost, with Kirill Eremenko

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