Is there any thumb of rule which scoring function should be used for e.g. the validation_curve?
Atm I try to study the difference between several optimizers:
validation_curve(grid_best , X_train , y_train , param_name = 'Adam', 'SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adamax', 'Nadam'] , param_range = param_range , cv=tscv , scoring="explained_variance" , verbose = 1 , n_jobs = n_jobs )
I use the
explained_variance but I think the function has to be minimized cause the values are mostly below zero. That's why I think the explained variance does not make sense here?
When I use
r2 I get the following curve:
Is that normal?