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survival_xgboost.html
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<meta name="description" content="We implement loss functions for learning accelerated failure time
(AFT) models in XGBoost, a leading machine learning library for gradient boosting." />
<title>
Survival regression with accelerated failure time model in XGBoost
</title>
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<body>
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itemtype="http://www.schema.org/ScholarlyArticle">
<p>[<a href=".">← Go back to profile</a>]</p>
<h1 itemprop="name">
Survival regression with accelerated failure time model in XGBoost
</h1>
<p>
with
<a href="https://avinashbarnwal.github.io/">
Avinash Barnwal</a> and
<a href="https://tdhock.github.io/">Toby D. Hocking</a>
</p>
<p>
Article published in
<a href="https://www.tandfonline.com/journals/ucgs20">Journal of Computational and Graphical Statistics</a> (2022)
</p>
<h2>Download</h2>
<ul>
<li>Preprint
[<a href="https://arxiv.org/abs/2006.04920">arXiv:2006.04920</a>]</li>
<li>Taylor & Francis Online
[<a href="https://doi.org/10.1080/10618600.2022.2067548">doi:10.1080/10618600.2022.2067548</a>]
</li>
</ul>
<h2>Synopsis</h2>
<p itemprop="description">
Survival regression is used to estimate the relation between time-to-event and feature
variables, and is important in application domains such as medicine, marketing, risk
management and sales management. Nonlinear tree based machine learning algorithms
as implemented in libraries such as XGBoost, scikit-learn, LightGBM, and CatBoost
are often more accurate in practice than linear models. However, existing state-of-the-art
implementations of tree-based models have offered limited support for survival
regression. In this work, we implement loss functions for learning accelerated failure time
(AFT) models in XGBoost, to increase the support for survival modeling for different
kinds of label censoring. We demonstrate with real and simulated experiments the
effectiveness of AFT in XGBoost with respect to a number of baselines, in two respects:
generalization performance and training speed. Furthermore, we take advantage of the
support for NVIDIA GPUs in XGBoost to achieve substantial speedup over multi-core
CPUs. To our knowledge, our work is the first implementation of AFT that utilizes
the processing power of NVIDIA GPUs. Starting from the 1.2.0 release, the XGBoost
package natively supports the AFT model. The addition of AFT in XGBoost has had
significant impact in the open source community, and a few statistics packages now
utilize the XGBoost AFT model.
</p>
<h2>Publication Details</h2>
<ul>
<li>
Journal article:<br>
Avinash Barnwal, Hyunsu Cho, and Toby Hocking. “Survival Regression with
Accelerated Failure Time Model in XGBoost,” <em>Journal of Computational
and Graphical Statistics</em>, 31:4, p. 1292-1302, May 24, 2022.
</li>
</ul>
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