📖 Documentation: bbstats.github.io/chimeraboost
- 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)
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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
- Exceedingly opinionated GBDT library that only depends on common Python libraries
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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
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Why?
- I want to be able to modify my GBDT library at will
- I know Python and I don't know C