XGBoost: A Scalable Tree Boosting System

[koptimizer]

📋 논문의 정보를 알려주세요.

• XGBoost A Scalable Tree Boosting System
• Tianqi Chen & Carlos Guestrin
• KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
• 2016-08

📃 Abstract

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.

🔎 어떤 논문인지 소개해주세요.

• Scalable한 머신러닝 프레임웍인 XGboost에 대한 논문입니다.
• 경진대회나 각종 분석에서 당연하게 썻던 프레임웍이라서 도데체 어떻게 만들었길래 그렇게 성능이 좋은 것인지 확인해보려고 합니다.

🔑 핵심 키워드를 적어주세요.

• Scalable, Gradient boosting, Sparsity-aware, parallel

📎 URL

• https://dl.acm.org/doi/abs/10.1145/2939672.2939785