normalization/automodel/ deep learning

francc

Hello I'm a beginner using rapidminer and I normalized my data in 2 different ways, but when I use "Automodel" I get the same results.

I'm using deep learning and I know that this method (in rapidminer) applies standardization by default when automodeling. I' m trying to do a regression and no matter how I introduce the dataset to train my model (without normalization, z transformation, range transformation), I always get the same results.

So, I want to know why this happen, any guidance will be helpful! Thanks
(maybe it is some Deep learning feature I'm ignoring)

(by the way, I love the platform)

Marco_Barradas

Hi @francc thanks for the good comments on the platform.
Do you get the same result when you apply the model or when you are learning the model?
If your problem is when you apply the model please check if you are using the Group Model operator and that you connected the models in the order that they were applied to the data. If you are using it inside a cross validation that model would be passed to the validation portion and will apply the same transformation on the data that it would recieve.