**ChefBoost** is a lightweight decision tree framework for Python **with categorical feature support**. It covers regular decision tree algorithms: [ID3](https://sefiks.com/2017/11/20/a-step-by-step-id3-decision-tree-example/), [C4.5](https://sefiks.com/2018/05/13/a-step-by-step-c4-5-decision-tree-example/), [CART](https://sefiks.com/2018/08/27/a-step-by-step-cart-decision-tree-example/), [CHAID](https://sefiks.com/2020/03/18/a-step-by-step-chaid-decision-tree-example/) and [regression tree](https://sefiks.com/2018/08/28/a-step-by-step-regression-decision-tree-example/); also some advanved techniques: [gradient boosting](https://sefiks.com/2018/10/04/a-step-by-step-gradient-boosting-decision-tree-example/), [random forest](https://sefiks.com/2017/11/19/how-random-forests-can-keep-you-from-decision-tree/) and [adaboost](https://sefiks.com/2018/11/02/a-step-by-step-adaboost-example/). You just need to write **a few lines of code** to build decision trees with Chefboost.

The easiest way to install ChefBoost framework is to download it from [from PyPI](https://pypi.org/project/chefboost). It's going to install the library itself and its prerequisites as well.