# which scoring function for validation_curve (regression)?

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_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?

I'd use r2 for validating regression. It'd be much easier to compare different models. Here's wiki, here's sklearn.

• Any explanation why R^2 over others? :)
– Ben
Nov 21, 2019 at 11:22
• It's not r square / r^2, it's just r2. It's much easier to compare this metric to other splits/experiments, as it converges to 1, 0 is constant answer, below 0 you're just bad. Nov 22, 2019 at 9:07
• sure, but why not e.g. rmse?
– Ben
Nov 22, 2019 at 9:25
• It's just easier to compare values that limits to 1. With rmse it's harder to interpret results in a meaningful way. Nov 22, 2019 at 10:46