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
Version 0.1.58 fixes vector accumulation rounding and the Windows C++ export test loader. It includes the vector-leaf functionality from the retained 0.1.57 tag, whose PyPI and GitHub release publication was withheld after CI failures.
Highlights
- Regression, classification, query-group ranking, survival, multi-output, multilabel, and callable-objective training.
- Low-level
Pool/trainAPIs 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.
Compact multiclass vector leaves
CTBoost 0.1.58 includes compact multiclass vector leaves introduced in the
0.1.57 source tag. Use
CTBoostClassifier(multi_strategy="multi_output_tree") to store one shared
CPU tree per multiclass boosting round, with one score per class in each leaf.
It preserves CTBoost's conditional-inference feature tests and split selection;
the default scalar-tree layout remains unchanged.
See the vector-leaf guide for supported workflows, artifact compatibility, and a reproducible comparison.
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 published TabArena artifacts measure CTBoost 0.1.56 on all 51 TabArena-v0.1 Lite datasets: one default configuration, one outer split per dataset, and eight-fold bagging. The local evaluation reports 1166.7 Elo (+52.1 / -67.5), a 0.3957 win rate, and position 23/38 among default-configuration rows, with no imputed CTBoost tasks. This is a default-only Lite result, not an official leaderboard entry or a measurement of 0.1.58. Elo depends on the comparison roster; Kaggle timings use different resources from the canonical benchmark and are not directly comparable.
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. See the 0.1.58 release notes for vector-leaf support, correctness fixes, and compatibility boundaries.
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.
1 maintainer
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