Occam's Razor (Parsimony ) in Machine Learning | Model Selection

Occam's Razor (Parsimony ) is one hypothesis that states that out of all possible models that provides similar results (or performance), the one that is most simple should be selected as the final model.



It dates back to many centuries ago when it was studied,not in relation to ML though but was studied in general. This is now widely accepted means of selecting the best model out of many models