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Conditional inference, boosted

CTBoost is a gradient-boosting library built around conditional-inference trees. It combines alpha-stopped feature-test selection, optional per-node Bonferroni adjustment, a familiar Python and scikit-learn interface, native C++ training, and optional CUDA acceleration.

Install CTBoost See benchmark status

Project status

CTBoost is an alpha project. Its API and model format are tested extensively, but it does not yet have the independent production history of CatBoost or XGBoost. Benchmark claims on this site state their dataset and protocol scope.

  • :material-chart-tree:{ .lg .middle } A different tree learner

    Conditional tests select splits before cut points are optimized. CTBoost keeps that defining behavior instead of copying symmetric or conventional greedy trees.

  • :material-speedometer:{ .lg .middle } Native CPU and CUDA paths

    Histogram training, stochastic boosting, DART, early stopping, callbacks, ranking, survival objectives, and task-aware feature preprocessing.

  • :material-shield-check:{ .lg .middle } Auditable artifacts

    Versioned inference manifests, deterministic fingerprints, JSON/Python/C++/ONNX exports, exact empirical interventional TreeSHAP, and reproducible benchmark metadata.

  • :material-language-python:{ .lg .middle } Python ecosystem fit

    NumPy, pandas, SciPy sparse, Arrow, Polars, cuDF/CuPy/DLPack adapters, scikit-learn helpers, and Dask/Ray/Spark integration surfaces.

A small example

from ctboost import CTBoostClassifier

model = CTBoostClassifier(
    iterations=800,
    learning_rate=0.05,
    max_depth=6,
    ordered_ctr=True,
    random_seed=42,
)
model.fit(
    X_train,
    y_train,
    eval_set=(X_valid, y_valid),
    early_stopping_rounds=50,
)

probability = model.predict_proba(X_test)
explanation = model.predict_shap_values(X_test, X_train)

Where to go next