I am trying to predict pseudo-random numbers using the past numbers with a multiplayer perceptron. The error while training is very low. However, as soon as I test it with a test set, the model overfits and returns very bad results. The correlation coefficient and error metrics are both not performing well.
What would be some of the ways to solve this issue?
For example, if I train it with 5000 rows of data and test it with 1000, I get:
Correlation coefficient 0.0742 Mean absolute error 0.742 Root mean squared error 0.9407 Relative absolute error 146.2462 % Root relative squared error 160.1116 % Total Number of Instances 1000
As mentioned, I can train it with as many training samples as I want and still have the model overfits. If anyone is interested, I can provide/generate some data and post it online.