I understand that you are observing an R-value of 0.95 for raw data, which drops to 0.85 for normalized data in your Artificial Neural Network (ANN). I would like to clarify whether these values are from the training dataset or the testing dataset or the validation dataset.
In general, the higher R-value associated with raw data might indeed suggest overfitting, potentially stemming from inadequate feature scaling. By normalizing the data, you are likely achieving more effective feature scaling, which can help prevent overfitting. Consequently, the decrease in the R-value for your Artificial Neural Network model when using normalized data could signify a more robust and generalizable model. This shift in R-value may indicate that the model is now better at capturing underlying patterns within the data, leading to improved generalization.
I hope this helps.
