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serengil/chefboost ★ 488 101 APPLICATION

ChefBoost is a Python library implementing decision tree algorithms (ID3, C4.5, CART, CHAID, regression) and ensemble methods (AdaBoost, Gradient Boosting, Random Forest). It provides a simple API for building models from pandas DataFrames and making predictions. The library handles both categorical and numerical features without requiring preprocessing. Architecture consists of modular components: main interface (Chefboost.py), utilities (commons/), core training algorithms (training/), and ensemble methods (tuning/). Key strengths include proper packaging, unit tests, documentation, and solutions to technical challenges like recursive multiprocessing. The implementation correctly follows standard ML algorithms while providing a usable framework for developers.

RATING: 7.5/10 MAINT: active DRIFT: low
#decision-tree #machine-learning #ensemble-methods #python-library #id3 #c45 #cart #chaid #random-forest #adaboost #gradient-boosting #decision-trees #gradient-boosting-machine #c45-trees #regression-tree #gbm #data-mining #gradient-boosting-machines #data-science #kaggle #gbdt #gbrt #python #categorical-features
▸ 2 reusable components found
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