
XGBoost: A Scalable Tree Boosting System - laudney
http://arxiv.org/abs/1603.02754
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laudney
"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."

