Is it acceptable to scale target values for regressors?
It is actually a common practice to scale target values in many cases.
For example a highly skewed target may give better results if it is applied log or log1p transforms. I don't know the characteristics of your data, but there could a transformation that might decrease your RMSE.
Secondly, Test set is meant to be a sample of unseen data, to give a final estimate of your model's performance. When you see the unseen data and tune to perform better on it, it becomes a cross validation set.
You should try to split your data into three parts, Train, Cross-validation and test sets. Train on your data and tune parameters according to it's performance on cross validation and then after you are done tuning, run it on the test set to get a prediction of how it works on unseen data and mark it as the accuracy of your model.
I am getting very high RMSE and MAE for MLPRegressor , ForestRegression and Linear regression with only input variables scaled (30,000+) however when i scale target values aswell i get RMSE (0.2) , i will like to know if that is acceptable thing to do.
Secondly is it normal to have better R squared values for Test (ie. 0.98 and 0.85 for train)
Thank You