Overfitting

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Overfitting/Overtraining in supervised learning (e.g. neural network). Training error is shown in blue, validation error in red. If the validation error increases while the training error steadily decreases then a situation of overfitting may have occurred.

In statistics, overfitting is fitting a statistical model that has too many parameters. An absurd and false model may fit perfectly if the model has enough complexity by comparison to the amount of data available. Overfitting is generally recognized to be a violation of Occam's razor.

The concept of overfitting is important also in machine learning. Usually a learning algorithm is trained using some set of training examples, i.e. exemplary situations for which the desired output is known. The learner is assumed to reach a state where it will also be able to predict the correct output for other examples, thus generalizing to situations not presented during training (based on its inductive bias). However, especially in cases where learning was performed too long or where training examples are rare, the learner may adjust to very specific random features of the training data, that have no causal relation to the target function. In this process of overfitting, the performance on the training examples still increases while the performance on unseen data becomes worse.

In both statistics and machine learning, in order to avoid overfitting, it is necessary to use additional techniques (e.g. cross-validation, early stopping), that can indicate when further training is not resulting in better generalization. The process of overfitting of neural network during the training is also known as overtraining. In treatment learning, overfitting is avoided by using a minimum best support value.

[edit] Literature

• Tetko, I.V.; Livingstone, D.J.; Luik, A.I. Neural network studies. 1. Comparison of Overfitting and Overtraining, J. Chem. Inf. Comput. Sci., 1995, 35, 826-833