I am using neural network for a binary classification problem (yes or no). My training data set is not that big (39,000 records). After using SMOTE to balance the target, I have 50 input variables that are all numerical. There are 3 hidden layers with 100, 50, 10 neurons respectively. When I train neural network, I get a very different outcome, in terms of accuracy, AUC, and the confusion matrix. Why is this and what can I do to improve my model?
UPDATE: My class(yes or no) is not unbalanced because I used smote to balance it. It was very imbalanced initially. And what I meant is every time I train, I get a different outcome.