chimeraboost
pronounced kai-MEER-uh-boost
Lightning-fast Gradient Boosting, near-Catboost quality, all in Python
📖 Documentation: bbstats.github.io/chimeraboost
#2 On TabArena GBDT Elo (Defaults)
Installation
pip install chimeraboost
Quickstart
from chimeraboost import ChimeraBoostClassifier, ChimeraBoostRegressor
# quality picks the speed/accuracy trade-off: 1 is fastest - 5 is strongest,
# default is 3.
clf = ChimeraBoostClassifier(quality=4)
clf.fit(X, y, cat_features=[0, 1], sample_weight=w)
proba = clf.predict_proba(X_test)
reg = ChimeraBoostRegressor()
reg.fit(X, y)
What?
- Regression, quantile regression, binary and multiclass classification
- Fast training and inference, all in python
- Extremely high quality predictions
How?
- Bagging as a first-class feature (using
n_ensembles) - Automatic early stopping
- Automatic linear-leaf auditioning
- Replay mechanism for faster refitting after early stopping
- Gradient-matrix free multi-quantile split search
- numba is very fast
Why?
- I want to be able to modify my GBDT library at will
- I know Python and I don't know C
Documentation
Benchmarking
Want to help? Run either one, then open an issue or PR with the JSON.
pip install -e ".[bench,competitors]"
python benchmarks/run_benchmarks.py --synth --seeds 3 --save # quick, synthetic
python benchmarks/run_benchmarks.py --decide --seeds 3 --save # slower, 103 real datasets
Each writes benchmarks/results/<timestamp>.json:
{
"provenance": {"chimeraboost": "0.30.0", "platform": "Linux-6.1", "cpu_count": 12,
"libraries": {"catboost": "1.2.10", "lightgbm": "4.6.0"}},
"records": [
{"dataset": "diabetes", "model": "ChimeraBoost", "seed": 0,
"metrics": {"primary": -59.82, "rmse": 59.82}, "fit_time": 0.23}
]
}
Inspirations / Citations
Most ideas in ChimeraBoost were someone else's.
- CatBoost, Prokhorenkova et al., NeurIPS 2018. Oblivious trees (the tree type itself from Kohavi & Li, IJCAI 1995), ordered target statistics, feature combinations, ordered boosting, and the size-dependent automatic learning rate (
adaptive_learning_rate). - XGBoost, Chen & Guestrin, KDD 2016. Regularized objective, second-order split gain, Newton leaf estimation,
min_child_weight. - LightGBM, Ke et al., NeurIPS 2017. Histogram-based split finding, which that paper itself treats as prior art (McRank, Li et al. 2007; pGBRT, Tyree et al. 2011).
- Minimum Variance Sampling, Ibragimov & Gusev, NeurIPS 2019. Gradient-weighted row sampling (
subsample). - Quantized GBDT training, Shi, Ke et al., NeurIPS 2022. Integer gradient histograms (
quantize_gradients). - SketchBoost, Iosipoi & Vakhrushev, NeurIPS 2022, and GBDT-MO, Zhang & Jung. Vector leaves and the projected gradient used for multiclass and multi-quantile splits.
- Linear-leaf trees, Shi, Li & Li, IJCAI 2019 (arXiv:1802.05640). Piece-wise-linear regression trees (
linear_leaves); model trees back to Quinlan's M5, 1992. - 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).
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