arXiv (Cornell University)· 2018· Preprint
CatBoost: gradient boosting with categorical features support
- 1,354citations
- 2018year
Short summary
CatBoost is a new open-source gradient boosting library that handles categorical features and outperforms existing implementations on public datasets.
AI-generated from the title and abstract; the full text is not read.
Key points
- CatBoost is a new open-source gradient boosting library.
- It successfully handles categorical features.
- It outperforms existing public gradient boosting implementations on popular datasets.
- It includes faster GPU learning and CPU scoring implementations.
AI-generated from the title and abstract; the full text is not read.
Abstract
In this paper we present CatBoost, a new open-sourced gradient boosting library that successfully handles categorical features and outperforms existing publicly available implementations of gradient boosting in terms of quality on a set of popular publicly available datasets. The library has a GPU implementation of learning algorithm and a CPU implementation of scoring algorithm, which are significantly faster than other gradient boosting libraries on ensembles of similar sizes.
The authors' abstract, as published at the source. arXiv (Cornell University), 2018 · DOI ↗
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Field: Signal Processing
Signal ProcessingComputer Science