New questions and valuation of final results

Good again, I followed by testing and the truth I'm a little confused. By plotting the predicted values in closeloop confuses me a bit and I can not understand the results, When I get low is when R2tst generalizes better because if I get high values both in train, validation and test accuracy is great I just suspect overfitting occurs because it is very similar and it is displaced with respect to the graph of original targets. Practically the resusltados I posted in the previous post. If anyone can explain this I would really appreciate it as I think is the last step and the one that deserves special attention in order to interpret the data correctly. Thank you very much.

Answer 1

[Greg Heath]

Before you close the loop, use tr to closely examine the trn, val, and tst results. The order of importance is tst, val , trna (adjusted for reduced DOF) and trn.

If you are satisfied, close the loop and run the same data though to compare results.

If you are satisfied with those results, then consider new data.

Signs of overfitting:

Ntrneq >> Ndof = Ntrneq-Nw

R2trna << R2trn

R2tst, R2val << R2trn

Hope this helps.

Thank you for formally accepting my answer.

Greg

[FRANCISCO]

I missing check

Ntrneq >> Ndof = Ntrneq-Nw

R2trna << R2trn

Because R2tst, R2train R2val and have a very high value on the order of 98-97%. What worries me is that when you close the loop and check with 30 different inputs to the previous period of training, validation and test, the graph of the predictions I is displaced with respect to the targets of that period. Thank you very much Greg.