Though these tools might be interesting, I wish they had called this something else. This isn't at all related to the concept of hyperparameters which people commonly refer to as hyperparams. And in their copy, the only reference to hyperparameters seems to be misusing the term.<p>> This stems from an industry-wide realization that model performance is ultimately bounded by data quality, not just model architecture or hyperparameters.<p>Generally we think of model architecture + weights (parameters) as making up the model itself, and hyperparam(s|eters) are the more relevant to how one arrives at those weights -- and for this reason are more relevant to the efficacy of training than the performance of the resultant model.