Hi everyone, Can somebody explain is there a difference between training a model using

fit()

and training with

cross_validation(refit=True, ..)

? In other words is it acceptable to run

cross_validation(refit=True, ..)

instead of

fit()

, because with

refit=True

, the CV method is internally running

fit()

already? I understand, in the case of cross_validation the tail of input data will be split into validation sets depending on the

n_windows

,

step_size

, etc. The question is more about the equivalence of the

fit()

and training done inside

cross_validation()

. Is it ok to omit the

fit()

and go straight for

cross_validation(refit=True)

if the goal is to evaluate a methods performance on data?

Hello! For your goal, it is okay to go with cross-validation. This results in multiple windows where you can compare the predicted and actual values, so it will give you a more robust and representative evaluation of the model's performance.

@Marco thanks a lot )