Survival Regression with Accelerated Failure Time Model in XGBoost

Avinash Barnwal, Hyunsu Cho, Toby Hocking

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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 uses 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 use the XGBoost AFT model. Supplementary materials for this article are available online.

Original languageEnglish (US)
JournalJournal of Computational and Graphical Statistics
DOIs
StateAccepted/In press - 2022

Keywords

  • GPU computing
  • Gradient boosting
  • Open source
  • Survival analysis
  • XGBoost

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Discrete Mathematics and Combinatorics

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