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I don't think iskander though you were advocating using ML, but generally saying that the approaches _usually_ used in ML would not work in some datasets which

I don't think iskander though you were advocating using ML, but generally saying that the approaches _usually_ used in ML would not work in some datasets which are way too small. A 12-point dataset is not desirable _per se_, but sometimes it's all one has - as is the case, for example in health-related research.

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 ago
  And, 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 ago
    I 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]