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?
edit:
When I use r2
I get the following curve:
Is that normal?