Our feature importance scores are calculated by applying the leave-one covariate out (LOCO) algorithm . We discuss the algorithm here for completeness. It primarily relies on the change in the predictive power of a model due to the omission of a feature from the predictor set. The guiding principle is that an important feature in the model is one that, when removed, leads to an increase in predictive error and lower predictive power.