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In which case, you can't really afford train and test splits but you can look at approaches like leave-one-out to estimate what your sampling error looks like.
by cwyers 9y ago
In which case, you can't really afford train and test splits but you can look at approaches like leave-one-out to estimate what your sampling error looks like.
- jpeloquin 9y agoAnd, more generally, bootstrapping, which is a class of methods for re-sampling one's data in order to estimate uncertainty. https://en.wikipedia.org/wiki/Bootstrapping_(statistics) https://en.wikipedia.org/wiki/Bootstrapping_(statistics)
- cwyers 9y agoI think leave one out is actually a special case of k-fold cross validation where k is equal to the number of datapoints. But yeah, you can use bootstrapping, cross validation, the jackknife. There's a large toolset there, and I trust an analyst who explains what motivated them to choose a certain tool rather than one who just uses a tool because it's what they always use or it's expected of them.
- deleted 9y ago[deleted]