  In the context of right-censored data, we study the problem of predicting the
restricted time to event based on a set of covariates. Under a quadratic loss,
this problem is equivalent to estimating the conditional Restricted Mean
Survival Time (RMST). To that aim, we propose a flexible and easy-to-use
ensemble algorithm that combines pseudo-observations and super learner. The
classical theoretical results of the super learner are extended to
right-censored data, using a new definition of pseudo-observations, the
so-called split pseudo-observations. Simulation studies indicate that the split
pseudo-observations and the standard pseudo-observations are similar even for
small sample sizes. The method is applied to maintenance and colon cancer
datasets, showing the interest of the method in practice, as compared to other
prediction methods. We complement the predictions obtained from our method with
our RMST-adapted risk measure, prediction intervals and variable importance
measures developed in a previous work.
