chimeraboost
Lightning fast, near-CatBoost quality, all in Python
📖 Documentation: bbstats.github.io/chimeraboost
Install
pip install chimeraboost && chimeraboost-warmup
chimeraboost-warmup compiles the numba kernels once and caches them, so the first
fit is not several seconds slower than the rest. Re-run it after every upgrade, which
resets the cache. See
Deployment.
Quickstart
from chimeraboost import ChimeraBoostClassifier, ChimeraBoostRegressor
# classification. quality picks the speed/accuracy trade-off: 1 fastest .. 5 strongest,
# defaulting to 3.
clf = ChimeraBoostClassifier(quality=5)
clf.fit(X, y, cat_features=[0, 1], sample_weight=w)
proba = clf.predict_proba(X_test)
# regression (RMSE, MAE, Quantile, Huber, Poisson, Gamma, Tweedie, or your own)
reg = ChimeraBoostRegressor(loss="Quantile", alpha=0.9)
reg.fit(X, y)
What it is
An opinionated GBDT library that only depends on common Python libraries (NumPy, numba, scikit-learn, SciPy):
- Regression, quantile regression, binary and multiclass classification
- A whole predictive distribution from one booster, with quantiles that cannot cross
(
ChimeraBoostQuantileRegressor) - Categorical features handled natively (CatBoost-style ordered target statistics)
- Missing values handled directly, no imputation
- Automatic early stopping, with optional grouped splitting for the validation set
- Bagging as a first-class feature (
n_ensembles) - Exact SHAP explanations (
model.shap_values(X)). The oblivious tree structure makes interventional TreeSHAP cheap enough to compute exactly, with no sampling.
On the TabArena benchmark, the default model scores above XGBoost and LightGBM while training faster than either. CatBoost scores higher and takes considerably longer.
TabArena is a benchmark we never tune against. A second read on an independently audited 22-dataset suite, including how it is weighted and where it disagrees with us, is in docs/benchmarks.md.
Documentation
- Getting started: install and first models
- Recipes: categoricals, quantiles, bagging, custom losses, and more
- Parameters: every option, with defaults and guidance
- FAQ: common questions
Why?
- I want to be able to modify my GBDT library at will
- I know Python and I don't know C
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). - Conformalized quantile regression, Romano, Patterson & Candès, NeurIPS 2019. Distribution-free interval calibration (
conformalize). - TabArena, Erickson et al., NeurIPS 2025 (arXiv:2506.16791). The tabular benchmark used for evaluation.
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