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Because the prior on your parameters smooths out the prediction. Most cookbook techniques such as ridge regressions, cross-validation, etc have a Bayesian inte
by murbard2 12y ago
Because the prior on your parameters smooths out the prediction.
Most cookbook techniques such as ridge regressions, cross-validation, etc have a Bayesian interpretation as a prior on the parameter.
Bayesian techniques allow you to use all the data available.
That said, sometimes they are computationally expensive, and it's better to approximate them by using a test set.
- moultano 12y agoUnless you have an infinite regress on priors for your priors, and uncomputable Komolgorov penalties on the structure of your model, I think you need a test set. (This means you need a test set.)
- ced 12y agoThere's an interesting chapter in MacKay's book on Occam's razor. I'm not sure how I feel about it, but it's very thought-provoking.
- GFK_of_xmaspast 12y agoIf your priori are that strong, why bother with the data?
- Totient 12y agoYou don't need a validation set. I'm pretty sure you still want a test set.