I am new to neural network and have read many of your post. My question is how to get similar R value for model and test for the neural network.
My data was 530 x 81 and target was 530x1, then I use Collinearity and stepwise VIF selection method (in R) to screen the input features and reduced to 530x48;
With such small dataset, I used Bayesian Regularization algorithm with 70% for training and 30% for test, however, the problem is the R value for the training dataset and test are quite different. Train = 0.58, Test = 0.34 How could I get similar outcome for both train and test ?
I am aware that the training result looks not very good, but considering the nature of the dataset, the result was actuall very good if I can have both training and test R above 0.5.
netbr = fitnet(NeuronNo,'trainbr');
netbr.divideParam.trainRatio = 70/100;
netbr.divideParam.testRatio = 30/100;
netbr = train(netbr,T3,Price2);
br = netbr(T3);
perfbr = perform(netbr,br,Price2);