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Then you might have the problem of over-fitting the training set. See here for example: http://en.wikipedia.org/wiki/Overfitting http://en.wikipedia.org/wiki/O
by haffi112 14y ago
Then you might have the problem of over-fitting the training set.
See here for example: http://en.wikipedia.org/wiki/Overfitting http://en.wikipedia.org/wiki/Overfitting
- StavrosK 14y agoYou use the validation set precisely to avoid overfitting. I agree that a 90/10 split would be more reasonable.
- ZoFreX 14y agoYou could also use 10-fold cross validation[1] or similar where you rotate the training/validation sets. [1]: http://en.wikipedia.org/wiki/Cross-validation_%28statistics%29#K-fold_cross-validation http://en.wikipedia.org/wiki/Cross-validation_%28statistics%...
- msellout 14y agoMore training data helps prevent overfitting.