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

Relevant playlists: Machine Learning Codes and Concepts:    • Machine Learning Codes and Concepts (...   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: https://www.seevid.ir/fa/w/0YRg9DVl0AI Intro https://www.seevid.ir/fa/w/0YRg9DVl0AI Decision trees fundamental questions https://www.seevid.ir/fa/w/0YRg9DVl0AI 1- What features to start with and where to put the split? https://www.seevid.ir/fa/w/0YRg9DVl0AI 2- How to split the samples? presorted-histogram, GOSS and Greedy methods https://www.seevid.ir/fa/w/0YRg9DVl0AI 3- How to grow a tree? Depth-wise, level-wise, leaf-wise and symmetric https://www.seevid.ir/fa/w/0YRg9DVl0AI 4- How to combine the trees? bagging vs boosting https://www.seevid.ir/fa/w/0YRg9DVl0AI Evolution of XGboost https://www.seevid.ir/fa/w/0YRg9DVl0AI LightGBM and CatBoost https://www.seevid.ir/fa/w/0YRg9DVl0AI comparing XGBoost, LightGBM and CatBoost