Often, the performance of predictive models is focused on averages (e.g., the mean absolute error), and little attention is given to the distribution of errors (e.g, via the so-called violin plots) and to the inspection of the outliers, i.e., the data points that yield the largest prediction errors. Are these data points simply wrongly measured or could they herald some different physical mechanism that was not captured by the model trained to yield acceptable average errors? Scientifically, it is equally important for a ML model to yield predicted values for new data points and, concurrently, provide reliable uncertainty quantification (UQ). In other words, the model should be able to recognize if it can make a confident prediction solely from the input representation of a test data point, identifying whether it is similar to the data points used for training (interpolatory regime) or dissimilar (extrapolatory regime). The correct metric for assessing this similarity is, however, most often unknown and systematically finding it for a given ML model is one of the most difficult steps for a reliable uncertainty estimate.