  In this paper, we make an experimental comparison of semi-parametric (Cox
proportional hazards model, Aalen's additive regression model), parametric
(Weibull AFT model), and machine learning models (Random Survival Forest,
Gradient Boosting with Cox Proportional Hazards Loss, DeepSurv) through the
concordance index on two different datasets (PBC and GBCSG2). We present two
comparisons: one with the default hyper-parameters of these models and one with
the best hyper-parameters found by randomized search.
