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Some Strategies For Combating Overfitting

Cross-validation can help to combat overfitting, for example by using it to choose the best size of decision tree to learn. But it’s no panacea, since if we use it to make too many parameter choices it can itself start to overfit [17].

Besides cross-validation, there are many methods to combat overfitting. The most popular one is adding a regularization term to the evaluation function. This can, for example, penalize classifiers with more structure, thereby favoring smaller ones with less room to overfit. Another option is to perform a statistical significance test like chi-square before adding new structure, to decide whether the distribution of the class really is different with and without this structure. These techniques are particularly useful when data is very scarce. Nevertheless, you should be skeptical of claims that a particular technique “solves” the overfitting problem. It’s easy to avoid overfitting (variance) by falling into the opposite error of underfitting (bias). Simultaneously avoiding both requires learning a perfect classifier, and short of knowing it in advance there is no single technique that will always do best (no free lunch).

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[17] A. Y. Ng. Preventing “overfitting” of cross-validation data. In Proceedings of the Fourteenth International Conference on Machine Learning, pages 245–253, Nashville, TN, 1997. Morgan Kaufmann.

-- Pedro Domingos

from "A Few Useful Things to Know about Machine Learning"

Quoted on Thu Feb 26th, 2015