CTBoost

CTBoost is an alpha gradient-boosting library built around conditional-inference trees. At each node it selects a feature with a conditional statistical test before optimizing that feature's split point. A native C++17 core provides CPU training, optional CUDA acceleration, and Python/scikit-learn interfaces.

Documentation · Getting started · Benchmarks · Compatibility

Highlights

  • Regression, classification, query-group ranking, survival, multi-output, multilabel, and callable-objective training.
  • Low-level Pool/train APIs plus familiar scikit-learn estimators.
  • NumPy, pandas, SciPy sparse, Arrow, and Polars input, with fitted categorical, text, and embedding preprocessing.
  • Native CPU and CUDA training, plus Dask, Ray, and Spark barrier adapters; Spark retains an explicit collect-to-driver fallback for small jobs.
  • Snapshots, warm starts, staged prediction, callbacks, model selection, and deterministic inference manifests.
  • Exact empirical interventional TreeSHAP and JSON, Python, C++, C ABI, ONNX, pickle, and prepared-feature R/JVM inference choices.

Install

python -m pip install -U ctboost
python -m pip install -U "ctboost[sklearn,dataframes]"  # optional integrations

See the GPU installation guide for the released wheel, driver, architecture, and platform matrix. Inspect an installation with:

python -c "import ctboost; print(ctboost.build_info())"

Quick start

import numpy as np
from ctboost import CTBoostClassifier

X = np.array(
    [
        [0.2, 1.0],
        [0.8, 0.1],
        [0.1, 0.9],
        [0.9, 0.2],
        [0.3, 0.7],
        [0.7, 0.3],
    ],
    dtype=np.float32,
)
y = np.array([0, 1, 0, 1, 0, 1])

model = CTBoostClassifier(
    iterations=100,
    learning_rate=0.05,
    max_depth=4,
    random_seed=42,
)
model.fit(X, y)

probability = model.predict_proba(X)
labels = model.predict(X)

The getting-started guide covers validation sets, early stopping, categorical data, grouped feature tests, and the low-level API.

Evidence and project status

CTBoost is an alpha project. Its API and model formats are tested extensively, but it does not yet have the independent production history of CatBoost or XGBoost.

The latest measured 0.1.55 public-wheel TabArena result is a three-dataset protocol smoke test: 3/3 successful CTBoost outer splits and 1054.0 provisional Elo. It is not a full or official leaderboard entry; with only one outer split per dataset, its uncertainty is too wide for model-to-model ranking claims. The sanitized record contains the exact scope, hashes, metrics, and resource measurements.

The final-source 0.1.55 pre-registered grouped-statistic panel completed 294/294 isolated fits and 42/42 exact control checks. Grouped-8 recorded nine wins, no ties, and three losses with an observed 5.63% median primary-loss improvement (task-bootstrap 95% interval: -1.23% to +13.64%), but its 1.1708 median paired fit-time ratio exceeded the frozen 1.15 ceiling. Because the promotion gates were conjunctive, grouped-8 did not advance and the conditional TabArena scout was not run. The quadratic feature test remains the default; grouped testing is opt-in.

Read the benchmark status and split-statistics research ledger for protocols, limitations, and machine-readable evidence.

Documentation

Topic Guide
Installation and first model Getting started
Training, objectives, callbacks, and wrappers Training workflows
GPU wheels and runtime requirements GPU installation
Data, streaming, and schema metadata Data input
Categorical, text, and embeddings Feature preprocessing
SHAP, influence, and diagnostics Explainability
Dask, Ray, and Spark Distributed training
Persistence, exports, and CLI Deployment
Prepared-feature R/JVM inference Portable inference
Python symbols and signatures API reference
Source builds and tests Development

Development

python -m pip install -e ".[dev]"
pytest tests

See the development guide for native CMake builds, repository layout, and documentation checks.

Methodology and license

CTBoost draws on the conditional-inference framework of Hothorn, Hornik, and Zeileis (2006) and the modular partykit work of Hothorn and Zeileis (2015). The research ledger records the precise literature-to-implementation boundary.

Apache-2.0 licensed. See the license text.

Project links

Data verified by PyPI on Aug 14, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Latest release

1 maintainer

PyPI data
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Avatar for Maiernator from gravatar.com Maiernator

Credits

Author: CTBoost Contributors

GitHub Statistics

Data verified by PyPI on Aug 14, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

Apache Software License (Apache License)

Requires

Python >=3.8

Provides Extra

cli sklearn plot arrow polars dataframes dask ray spark onnx dev

Classifiers

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