Module 10- Theory 3: Advanced ML boosting techniques: XGboost, Catboost, LightGBM

Relevant playlists: Machine Learning Codes and Concepts: https://youtube.com/playlist?list=PL2GWo47BFyUNeLIH127rVovSqKFm1rk07&si=lCPyHenEQYBCJzQ_ Deep Learning Concepts, simply explained: https://www.youtube.com/playlist?list=PL2GWo47BFyUO6Fiy2mJCxR8sUrBEfT6BM Instructor: Pedram Jahangiry All of the slides and notebooks used in this series are available on my GitHub page, so you can follow along and experiment with the code on your own. https://github.com/PJalgotrader Lecture Outline: 0:00 Intro 0:44 Decision trees fundamental questions 3:55 1- What features to start with and where to put the split? 8:47 2- How to split the samples? presorted-histogram, GOSS and Greedy methods 13:55 3- How to grow a tree? Depth-wise, level-wise, leaf-wise and symmetric 18:26 4- How to combine the trees? bagging vs boosting 23:30 Evolution of XGboost 39:03 LightGBM and CatBoost 41:37 comparing XGBoost, LightGBM and CatBoost