Chefboost -- an alternative Python library for tree-based models

I randomly encountered chefboost in my Twitter feed and given that I never heard about it before, I decided to have a quick look into it and test it out. In this article, I will briefly present the library, mention the key differences from the go-to library which is scikit-learn, and show a quick example of chefboost in practice. I think the best description is provided in the library's GitHub repo: "chefboost is a lightweight decision tree framework for Python with categorical feature support". Following the last point, chefboost provides three algorithms for classification trees (ID3, C4.5, and CART) and one algorithm for regression trees. To be honest, I was not entirely sure which one is currently implemented in scikit-learn, so I checked the documentation (which also provides a nice and concise summary of the algorithms).