Skip to main content

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

chimeraboost-0.19.0.tar.gz (122.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

chimeraboost-0.19.0-py3-none-any.whl (77.4 kB view details)

Uploaded Python 3

File details

Details for the file chimeraboost-0.19.0.tar.gz.

File metadata

  • Download URL: chimeraboost-0.19.0.tar.gz
  • Upload date:
  • Size: 122.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.13

File hashes

Hashes for chimeraboost-0.19.0.tar.gz
Algorithm Hash digest
SHA256 607012076401b72a73caed184b35965410c51ac4c9289bc22b3c81cc6d2b9433
MD5 051cd9fd1ef317990499dd42143ceac8
BLAKE2b-256 7faba8a4e1e2a7031c0ff80930a848f32342a2c7518eba6acfe80d12d7e77750

See more details on using hashes here.

File details

Details for the file chimeraboost-0.19.0-py3-none-any.whl.

File metadata

  • Download URL: chimeraboost-0.19.0-py3-none-any.whl
  • Upload date:
  • Size: 77.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.13

File hashes

Hashes for chimeraboost-0.19.0-py3-none-any.whl
Algorithm Hash digest
SHA256 2a0814f8c79868e5e9e9628b73453061ef42f81797b33b6706355de2132ceba3
MD5 6c7a86274d46f02e63887060bd2822c1
BLAKE2b-256 38904254c8711d5661a87180684d9b20008e8bd113218ac532cafe6e8cc0da85

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page