"Rajarshi Guha" <[EMAIL PROTECTED]> wrote in message news:[EMAIL PROTECTED] > 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 ---------------------------------------------------------------------------- ------------------------------- I would also suggest you look into structural equation modeling (SEM), since this is a big field. The equations are basically covariance/correlation based, and use maximum likelhood methods to fit models to data. For a data matrix pxn, where p is the number of variables, up to p*(p+1)/2 parameters can be fitted. The method gives rise to many model fit parameters, chiefly Chi-Square based. There have been papers on differenent ways to arrive at a p value from correlation values.
David Heiser . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
