I know that Bayesian and frequentist approaches differ in their definition of probability.
Practically, in machine learning a model is a formula with tunable parameters.
Then the difference between Bayesian and frequentist is:
That the parameters are assumed to be fixed numbers in frequentist setting and the parameters have their own distributions in the Bayesian setting.
Am I missing anything here or anything is mis-interpreted?
I am not asking theoretical arguments, just what is the practical manifestation of frequentist vs Bayesian w.r.t. Machine learning models and their optimization/fitting.