MLPRegressor training versus validation loss

[weefuzzy]

Hi and welcome @doett

@doett

does “no longer improving” refer to the training loss? (it smoothes out and stops cause of no improv.) or does it refer to the validation loss? (when val loss goes up, overfitting starts… then it stops), so “early stopping” is built in and i dont need to plot validation loss…)

It refers to the validation loss so – in principle – you shouldn’t need to track that, and the object should stop early at a useful point.

I would like to develop some better testing and validation tools so that models can be more rigorously compared and evaluated etc. but haven’t yet really figured out what would be a musician-friendly interface (see Removing outliers while creating classifiers - #11 by weefuzzy for the start of a discussion about that)