"Rajarshi Guha" <[EMAIL PROTECTED]> wrote: "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)?" "CybercafeUser" <[EMAIL PROTECTED]> responded: "Yes it is. Look at almost all econometrics books" This is a classic case of "argumentum ad verecundiam". Whether R-squared is a "good indicator" of fit or not depends on the context of the problem- no measure is "best" for all purposes. R-squared is a measure of linear relation between variables. Some interesting commentary may be foudnd at: http://www.statisticalengineering.com/r-squared.htm http://faculty.mville.edu/derrellr/metrics%20notes/ap%20metrics%20f%2003%20notes%20chp%202.pdf -Will Dwinnell http://will.dwinnell.com . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
