ChefBoost
A lightweight Python decision tree framework supporting ID3, C4.5, CART, CHAID, regression trees, gradient boosting, random forest, and AdaBoost with categorical feature support.
What is ChefBoost?
ChefBoost is a Python framework for building decision tree models, including both classic algorithms like ID3 and C4.5 and advanced techniques like gradient boosting and random forest. It solves the problem of implementing tree-based models with minimal code while providing native support for categorical features, eliminating the need for manual encoding.
Data scientists, machine learning engineers, and researchers who need a straightforward, lightweight library for decision tree modeling in Python, especially when working with datasets containing categorical variables.
Developers choose ChefBoost for its simplicity, comprehensive algorithm support, and built-in handling of categorical features, which reduces pre-processing overhead. Its ability to generate interpretable rules and support parallel processing offers a practical balance between ease of use and performance.
Overview
A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python
Use Cases
Best For
- Quick prototyping of decision tree models with minimal code
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