Hello, I was wondering whether anybody whould be able to help with this query.
I have some neural network models which makes predictions for a dataset. When comparing various models we evalute the effectiveness by looking the RMS error and the value of R^2 between the predicted and actual values. However, I seem to have read somewhere that R^2 is not always a 'good indicator' - in that a data set can be randomly generated yet show a good R^2. Is this true? And if so, does anybody know how I can reference this (paper/book)? Thanks, Rajarshi . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
