Module 3- Part 2- ML boosting algorithms XGBoost, CatBoost and LightGBM

Relevant playlists:
Machine Learning Concepts, simply explained:    • Machine Learning Concepts (Simply Exp...  
Deep Learning Concepts, simply explained:    • Deep Learning Concepts (Simply Explai...  
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 Roadmap
2:12 What are the four fundamental questions in decision tree based models?
5:22 What features and cut off to start with?
10:12 How to split the samples (pre-sorted and histogram, GOSS, Greedy)
15:22 How to grow a tree? (Depth-wise, Leaf-wise, Symmetric)
19:52 How to combine trees (Bagging vs Boosting)
24:55 Evolution of XGBoost
34:48 XGBoost details
40:25 LightGBM details
41:15 CatBoost details
43:00 Comparing XGBoost, LightGBM and CatBoost