Neural Network - R value equal 1- Over-fitting or not?

Dear All;

I built a neural network model for regression as shown below but I got an R value equal 0.99998 which is almost 1. Does this indicate that I have overfitting? If yes, then how can I prevent or avoid overfitting?

% Read input and output vaules
data=readtable ('GOSP_DATA_Final.xlsx','sheet','Data');
I1=data.Ta;
I2=data.LPPT_P;
I=[I1 I2];
I=I';
T=data.LR;
T=T';

% Normalization between -1 and 1
[I,ps] = mapminmax(I);
[T,ts] = mapminmax(T);

% Building Network
h= 10;
net= fitnet(h);
[net,tr]  = train(net, I, T);
% Teting Neural networl
tInd= tr.testInd;
tstOutput=net(I(:,tInd));
tstOutput = mapminmax('reverse',tstOutput,ts);
T(tInd)= mapminmax('reverse',T(tInd),ts);
tstperformance = perform(net, T(tInd), tstOutput)
figure (1), 
plotregression(T(tInd),tstOutput);

Answer 1

[Srivardhan Gadila]

If the calculated R value is almost same for all the three Train, Test and Validation sets then your model is no near to Overfitting. If you observe that the calculated R for training set is more than that for validation and test sets then your network is Over fitting on the training set. You can refer to Improve Shallow Neural Network Generalization and Avoid Overfitting.

[Image Analyst]

@Srivardhan Gadila, they will all be different, always. I think the question is : how much different can the R of the Training set be from the R of the Validation or Test set before overfitting begins to be a concern?