Impossible to tell without knowing or being able to calculate the normalized degree-of-freedom-adjusted (DOFA) training subset MSE (NMSEtrna) or the corresponding Rsquare or coefficient of determination (Rsqtrna = 1-NMSEtrna).
Search Rsquare and "coefficient of determination" on Google and Wikipedia.
Calculations can be made as follows
[ I N ] = size(x)
[ O N ] = size(t) = size(y)
Ntrn = No. of training examples ( Ntrn ~ 0.7*N is default )
Ntrneq = Ntrn*O % No. of training equations
MSEtrn00 = mean(var(ttrn',1))% avg training target variance
SSEtrn = sse(ttrn-ytrn)
MSEtrn = SSEtrn/Ntrneq
NMSEtrn = MSEtrn/MSEtrn00
Rsqtrn = 1 - NMSEtrn
Adjustments ("a") for degrees of freedom lost when evaluating the net with the same data that was used to estimate the weights:
H = number of hidden nodes
Nw = (I+1)*H+(H+1)*O % No. of unknown weights
Ndof = Ntrneq - Nw % No. of DOF
MSEtrn00a = mean(var(ttrn',0))
NOTE: If there are more unknown weights than equations, Ndof < 0 and special methods like trainbr and/or regularization are required. Otherwise,
For DOFA with Ndof > 0:
MSEtrna = SSEtrn/Ndof
NMSEtrna = MSEtrna/MSEtrn00a
Rsqtrna = 1 - NMSEtrna
For many problems an appropriate training goal is
R2sqtrna >= 0.99
or
MSEtrn <= MSEtrngoal = 0.01*max(Ndof,0)*MSEtrn00a/Ntrneq
Hope this helps.
Thank you for formally accepting my answer
Greg
