ChefBoost

The ChefBoost Python framework supports decision trees such as ID3, C4.5, CART, CHAID, regression trees, and ensemble methods.

github.com/serengil/chefboost
ChefBoost homepage screenshot

About ChefBoost

ChefBoost is a lightweight decision tree framework for Python with categorical feature support. It covers ID3, C4.5, CART, CHAID and regression trees, plus gradient boosting, random forest and AdaBoost. You pass a pandas DataFrame with a config such as C4.5 to fit a model. Built trees are stored as Python if-statements and can predict new instances, be restored directly, and be saved for transfer learning.

Pricing is not clearly stated.

Key features

  • Supports ID3, C4.5, CART, CHAID and regression trees
  • Supports gradient boosting, random forest and AdaBoost
  • Handles numeric and nominal features without pre-processing
  • Accepts pandas DataFrame input with configurable algorithm
  • Exports decision trees as Python if-statements
  • Saves and restores trained models for reuse
Best for: Python users who need to build decision trees with categorical features.
Watch out for
  • Model restoration requires storing .py and .pkl files under outputs/rules.

ChefBoost at a glance

Type
Open-source project
Pricing
Not confirmed. Check the site
Details verified
Oct 22, 2026 · Official source
Open source
Yes · Source code
Language
English
Last checked
Oct 5, 2026 · Online when checked
Listed
Oct 5, 2026

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