Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: XGBoost: A Scalable Tree Boosting System   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.

17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat