The third observation is about removing signal outliers. Five of the eleven models preprocessed the training data by clearing out outliers, including the top two models. We tried this procedure with two good models that did not use such a procedure, Ferizi1 and Ferizi2, and observed that it did not affect the ranking, though it did improve the prediction error marginally. This is understandable, considering the relatively little weight these apparent outliers have on the total number of measurements (10 points from a 4812‐strong dataset). Additionally, specific strategies for predicting the signal, e.g. bootstrapping or cross‐validation, as used by the top two models of Ramirez‐Manzanares and Nilsson, may also help the model ranking.