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

Super Data Science: ML & AI Podcast with Jon Krohn

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 (https://www.readytensor.ai/), and by Data Universe, the out-of-this-world data conference (https://datauniverse2024.com). 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