Cheatsheet

I made a document which aims to summarize the important notions of tree-based methods : decision trees, pruning, bagging, random forest, boosting.

I will focus on specific libraries and new methods in another articles (e.g. XGBoost, CatBoost, LGBM)

Interesting ressources on tree-based methods :

• The course Statistical Learning available on the plateform Stanford Online (chapter 8). You can also find it on Youtube.

• You can also refer to the textbook An Introduction to Statistical Learning or Elements of Statistical Learning

• The videos of Kilian Weinberger on his Youtube channel . Lectures 28 to 34.

• Course notes of Kilian Weinberger on his website , 17 to 19.