Survival regression with accelerated failure time model in XGBoost

8 Jun 2020Avinash BarnwalHyunsu ChoToby Dylan Hocking

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... (read more)

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