I am standardizing the names of the models on my Beta Machine Learning Toolkits (BetaML)… concerning the Imputers (of missing values), I have some that use a specific algorithm, e.g. Random Forest, or Gaussian Mixture models (and I call them “RandomForestImputer” and “GaussianMixtureImputer”) but I have one that works with any supervised model (not necessarily from BetaML), as long this has an API of type Model(); a_fit_function(), a_predict_function(). Should I call this Imputer a “GeneralImputer”, “UniversalImputer”, “StandardImputer”, or what else? (I think I would avoid “GenericImputer” as it would imply it doesn’t specialize to one particular situation, while it does a sufficient job on all cases, while here it is the opposite, it can specialise to a particular case given a suitable model)