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15.20. Overfitting during Training

Applies to: NeuralTools 5.x–7.x

How does NeuralTools address the problem of overfitting?

Overfitting occurs when neural net "memorizes" the training data instead of finding general patterns. With an overfitted net, the testing results on the training set are very good, but they are poor when the net is tested on data that was set aside for testing.

This potential problem is addressed in NeuralTools as follows:

NeuralTools 6.0 added Sensitivity Analysis, which should help in diagnosing overfitting. If we get overfitting with a given architecture, that will probably show in unstable results from Sensitivity Analysis.

Last edited: 2015-09-03

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