Semantic Scholar Open Access 2016 51847 sitasi

XGBoost: A Scalable Tree Boosting System

Tianqi Chen Carlos Guestrin

Lihat Sumber DOI

Abstrak

Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.

Topik & Kata Kunci

Computer Science

Penulis (2)

Tianqi Chen

Carlos Guestrin

Format Sitasi

APA MLA BibTeX

Chen, T., Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. https://doi.org/10.1145/2939672.2939785

Akses Cepat

Lihat di Sumber doi.org/10.1145/2939672.2939785

Informasi Jurnal

Tahun Terbit: 2016
Bahasa: en
Total Sitasi: 51847×
Sumber Database: Semantic Scholar
DOI: 10.1145/2939672.2939785
Akses: Open Access ✓