Can the number of features vary? For example, say I want to predict a cat's age given the number of whiskers, fur density, and number of legs. However, sometimes I may only have number of whiskers and number of legs, but not fur density. Would that require its own, separately trained, MLModel?
This is such a cool example! Thanks for the question. Have you tried using the tabular classifiers in Create ML? When you have missing data in your feature columns you can try replacing them (imputing). The TabularData framework makes this part really easy.