Title: XGBoost: A Scalable Tree Boosting System
Authors: Tianqi Chen, Carlos Guestrin
Published: 9th March 2016 (Wednesday) @ 01:11:51
Link: http://arxiv.org/abs/1603.02754v3
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.