# Survival regression with accelerated failure time model in XGBoost

@article{Barnwal2020SurvivalRW, title={Survival regression with accelerated failure time model in XGBoost}, author={Avinash Barnwal and Hyunsu Cho and Toby Hocking}, journal={ArXiv}, year={2020}, volume={abs/2006.04920} }

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 implementations of tree-based models have offered limited support for survival regression… Expand

#### 3 Citations

Optimizing ROC Curves with a Sort-Based Surrogate Loss Function for Binary Classification and Changepoint Detection

- Computer Science, Mathematics
- ArXiv
- 2021

This work proposes a convex relaxation of this objective that results in a new surrogate loss function called the AUM, short for Area Under Min(FP, FN), which requires a sort and a sum over the sequence of points on the ROC curve and is efficiently computed and used in a gradient descent learning algorithm. Expand

Analysis of overweight vehicles on asphalt pavement performance using accelerated failure time models

- Environmental Science
- International Journal of Pavement Engineering
- 2021

Vehicle overweighting has been one of the key driving forces of deteriorated pavements. The overweighting characteristics that dominate the pavement performance deterioration have been inadequately...

Computing the Hazard Ratios Associated with Explanatory Variables Using Machine Learning Models of Survival Data

- Medicine, Computer Science
- JCO clinical cancer informatics
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A novel way to compute HRs from tree-based ML models using the SHapley Additive exPlanation values, which is a locally accurate and consistent methodology to quantify explanatory variables' contribution to predictions. Expand

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