Copula-based conformal prediction for multi-target regression

There are relatively few works dealing with conformal prediction for multi-task learning issues, and this is particularly true multi-target regression. This paper focuses on the problem of providing valid (i.e., frequency calibrated) multi-variate predictions. To do so, we propose to use copula functions inductive prediction, illustrate our proposal by applying it deep neural networks random forests. We show that proposed method ensures efficiency validity regression problems various data sets.