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CatBoost-inspired gradient boosting in pure Python with a numba backend

Project description

chimeraboost

What if CatBoost was slightly worse, 2–6× faster, and all in Python?

📖 Documentation: bbstats.github.io/chimeraboost

chimeraboost logo
  • Installation
pip install chimeraboost
  • Sample code:
from chimeraboost import ChimeraBoostClassifier, ChimeraBoostRegressor

# classification (n_ensembles=8 is the benchmarked accuracy mode; avoid 2)
clf = ChimeraBoostClassifier(early_stopping=True, n_ensembles=8)
clf.fit(X, y, cat_features=[0, 1], sample_weight=w)
proba = clf.predict_proba(X_test)

# regression (RMSE, MAE, or Quantile)
reg = ChimeraBoostRegressor(loss="Quantile", alpha=0.9, early_stopping=True, n_ensembles=8)
reg.fit(X, y)

TabArena-Lite Elo vs speed Pareto

  • What?

    • Exceedingly opinionated GBDT library that only depends on common Python libraries
      • Categorical features (catboost-like processing) and sample weights
      • Bagging as a first-class feature (n_ensembles)
      • Automatic early stopping, with optional grouped splitting for the validation set
    • Supports regression, quantile regression, binary and multiclass classification
    • Exact SHAP explanations (model.shap_values(X)) — interventional TreeSHAP computed exactly thanks to the oblivious tree structure
  • Inspirations / Citations

    • CatBoost — Prokhorenkova et al., NeurIPS 2018 — ordered boosting, ordered target statistics, oblivious trees
    • XGBoost — Chen & Guestrin, KDD 2016 — regularized objective, Newton leaf estimation, column subsampling
    • LightGBM — Ke et al., NeurIPS 2017 — histogram-based split finding
    • Linear-leaf trees — Shi et al., IJCAI 2019 (arXiv:1802.05640) — piece-wise-linear regression trees (linear_leaves)
    • TreeSHAP — Lundberg et al., Nature Machine Intelligence 2020 (orig. SHAP, NeurIPS 2017) — exact additive feature attributions (shap_values)
    • OpenFE — Zhang et al., ICML 2023 (arXiv:2211.12507) — automated pairwise feature generation (cross_features)
    • TabArena — Erickson et al., NeurIPS 2025 (arXiv:2506.16791) — tabular benchmark used for evaluation
  • Why?

    • I want to be able to modify my GBDT library at will
    • I know Python and I don't know C

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