# CatBoost

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Repository: https://github.com/catboost/catboost
Canonical: https://ross.abutalabs.com/products/catboost
Homepage: https://catboost.ai
Language: C++
License: Apache-2.0
License Family: permissive
Topics: machine-learning, decision-trees, gradient-boosting, gbm, gbdt, python, r, kaggle, gpu-computing, catboost, tutorial, categorical-features, gpu, coreml, data-science, big-data, cuda, data-mining
Last push: 2026-08-26T15:39:19+00:00
Link (homepage): https://catboost.ai
Link (site_page): https://catboost.ai/docs
Link (site_page): https://catboost.ai/docs/concepts/python-installation.html
Link (site_page): https://catboost.ai/docs/concepts/tutorials.html

## Health v2 (maintenance only)
Score: 85/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 59, longevity 100
- inputs: {"age_days": 3333, "days_push": 7, "days_rel": 193, "gap_med": 46.0, "n_releases_24m": 7}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 9078, forks 1324 (observed 2026-08-28T04:10:28.250094+00:00)

## What it is
CatBoost is an open-source gradient boosting on decision trees library developed by Yandex, offering high-quality classification, regression, and ranking models with native categorical feature support. It provides fast CPU and multi-GPU training with bindings for Python, R, Java, C++, Rust, Node.js, and Spark.

## Use cases
- train a gradient boosting model for tabular data classification
- handle categorical features without manual preprocessing
- train gradient boosted trees on GPU
- build a ranking model for search or recommendations
- export a trained model to ONNX or CoreML for fast inference
- run distributed gradient boosting training with Spark
- compete in Kaggle tabular machine learning competitions

## When to choose
- your dataset has many categorical features and you want strong default accuracy
- you need fast GPU or multi-GPU training for gradient boosting
- you need low-latency model inference in C++ or other languages
- you want distributed training via Spark or CLI

## When to avoid
- you need deep learning on unstructured data like images or text
- you want a lightweight pure-Python scikit-learn-style tool
- your team is already standardized on XGBoost or LightGBM with no need for categorical handling

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, gpu-computing, data-science
- domain: machine-learning, data-science, artificial-intelligence
- platform: python, cpp, cross-platform, cli, jvm
- tags: gradient-boosting, decision-trees, gbdt, categorical-features, cuda, ranking, classification, regression, spark, model-export, gpu

## Member repositories
- catboost/catboost (main) score 85
- catboost/tutorials (docs) score 64

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:28.250094+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:24:00.797783+00:00, confidence not recorded.
  - readme: https://github.com/catboost/catboost (fetched 2026-08-28T04:10:28.250094+00:00, sha b66faa37eb51)
  - homepage: https://catboost.ai (fetched 2026-08-29T08:24:05.931153+00:00, sha 3bf5e6d722ca)
  - site_page: https://catboost.ai/docs (fetched 2026-08-29T08:24:05.966102+00:00, sha 8b98019287e3)
  - site_page: https://catboost.ai/docs/concepts/python-installation.html (fetched 2026-08-29T08:24:05.980897+00:00, sha 73691615613d)
  - site_page: https://catboost.ai/docs/concepts/tutorials.html (fetched 2026-08-29T08:24:05.982904+00:00, sha d428cb3d8989)
- Data as of 2026-08-30T08:39:29.467469+00:00.