· 2016
XGBoost
- 49,653citations
- 2016year
Short summary
XGBoost is a novel, scalable tree boosting system designed for high performance on large datasets, achieving state-of-the-art results in machine learning challenges.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces XGBoost, a scalable tree boosting system.
- Features a sparsity-aware algorithm and weighted quantile sketch for approximate tree learning.
- Includes optimizations for cache access, data compression, and sharding.
- Scales to billions of examples with reduced resource usage.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source., 2016 · DOI ↗
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Field: Artificial Intelligence
Artificial IntelligenceComputer Science