CatBoost: High performance gradient boosting on decision trees library
Abstract
CatBoost is a machine learning method based on gradient boosting over decision trees and can be used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. It supports both numerical and categorical features and computation on CPU and GPU, and is fast and scalable. Visualization tools are also included in CatBoost.
- Publication:
-
Astrophysics Source Code Library
- Pub Date:
- August 2021
- Bibcode:
- 2021ascl.soft08008C
- Keywords:
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- Software