I am building a predictive model (Neural Network) of interval data using PROC NEURAL.  I have run into a difficulty while trying to use Fit Statistics to help select which of a set of candidate models is 'best.'     One option in PROC NEURAL (EM 14.1) is to output fit statistics (e.g. TRAIN outfit = data-set-name). The definitions (and some formulae) of these fit statistics are not easy to locate, but are listed in part here:   http://support.sas.com/documentation/cdl/en/emxndg/67980/HTML/default/viewer.htm#n002icfvzhfd57n1c2pmqx1ygovl.htm     and here:   http://support.sas.com/documentation/cdl/en/emxndg/67980/HTML/default/viewer.htm#n0qzrycxmdg039n1kwz7op5j06bh.htm     Generally the variable names listed in this second link correspond (in some fashion - typically abbreviations) to a longer descriptive name of the variable.  However, this 2nd link lists _AIC_ and _SBC_ as follows:    Fit Statistics Computed According to the Error Function Name Label _AIC_ Sum of Frequencies _AVERR_ Total Degrees of Freedom _ERR_ Divisor for ASE _SBC_ Train: Average Squared Error     The first link above gives an equation for SBC that is not "Average Squarred Error," but rather the generally accepted use of Schwarzs' Bayesian Criterion, and is in line with the definition (and formula) in PROC GLMSELECT (see: http://documentation.sas.com/?docsetId=statug&docsetVersion=14.2&docsetTarget=statug_glmselect_details15.htm&locale=en).  Are the four PROC NEURAL fit statistics from the second link, and in the copied table therefrom (above), simply mis-defined in teh second link, and earlier documentation (i.e.this just a typo in the EM developers notes)?  In: SAS(R) Enterprise Miner(TM) 14.1 Extension Nodes: Developer's Guide  /  Predictive Modelling  /  Input and Output Data Sets   More generally, do the definitions and formulae used in PROC NEURAL fit statistics generally line up with those used for PROC GLMSELECT?  In the first link above there is reference to the formulae 'adjusting' for the type of training used (e.g. least squares vs. maximum liklihood) for SBC (on Predictive Modelling - Generalization). This suggests that in PROC NEURAL fit statistics, _SBC_ is indeed "SBC" and not "Average Squared Error."    Is this also true for _AIC_ (that it is indeed Akaike's Information Criterion, not "Sum of Frequencies")?     If so, what are the formulae for AIC for each type of training ( least squares, maximum likelihood, or M-estimation -  i.e.  NETOPTIONS OBJECT = DEV, LIKE, or MEST)?    John ... View more