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 0.1.59 adds optional loss backtracking, coupled multiclass leaf fitting, and joint multiclass feature tests. It also reduces grouped-test allocation overhead and corrects feature-test versus cut-search profiling. Existing learning defaults and the statistical feature-selection-before-cut contract remain.
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
For the new CPU learning controls, see safeguarded leaves and joint multiclass tests. The full solver and joint test support 3–32 classes and remain opt-in.
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 0.1.58 on all 51 TabArena-v0.1 Lite datasets, using the
default plus 25 frozen HPO configurations, one outer split (r0f0), and
eight-fold bagging. All 1,326 parent results and 10,608 child fits completed,
with no imputed CTBoost tasks.
| Evaluation | Lite Elo |
|---|---|
| Default | 1161.9 |
| Tuned | 1262.8 |
| Tuned + ensemble | 1296.9 |
These local Lite scores use an 87-row comparison roster; they are not an official leaderboard entry or a TabArena-Full result. Kaggle used 4 CPUs and 28 GB RAM, so its timings are not directly comparable to the canonical benchmark. The benchmark history retains the earlier 0.1.56 default-only result and explains the different rosters.
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 0.1.59 release notes for the new learning controls and their validation. The TabArena scores above belong to 0.1.58; 0.1.59 has not yet been evaluated on that benchmark.
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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