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"When there are less than 200 examples, the training set and validation set have the same size. Otherwise, the training set is composed of 100 examples and the
by curiousdannii 14y ago
"When there are less than 200 examples, the training set and validation set have the same size. Otherwise, the training set is composed of 100 examples and the other examples constitute the validation set."
Why divide the data into sets like that? Assuming the sample data is representative, wouldn't it be better to use much more for training, say 90% with 10% for validation?
- haffi112 14y agoThen 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.
- Cyranix 14y agoThe other replies, directly discussing overfitting, are spot on. However, I did want to point out explicitly that the stated assumption of representative sample data can be a fundamental challenge, not to be taken lightly.