I have read that leave-one-out cross-validation provides a relatively “unbiased estimate of the true generalization performance” (e.g. here) and that this is an advantageous property of the leave-one-out CV.

However, I don't see how this follows from the properties of leave-one-out CV. Why is the bias of this estimator low when compared to others?


I keep investigating the topic, and I believe it has to do with the fact that this estimator is less pessimistic than, say, K-fold validation, since it uses all the data but one instance, but it would be great to read a mathematical derivation of this.


1 Answer 1


I don't think there is a need for a mathematical derivation of the fact that in ML, with increasing training test size, the prediction error rates decrease. LOO -- compared to k-fold validation -- maximizes the training set size, as you have observed.

However, LOO can be sensitive to "twinning" -- when you have highly correlated samples, with LOO you have the guarantee that for each sample used as a test set, the remaining "twins" will be in the training set. This can be diagnosed by a rapid decrease in accuracy when LOO is replaced by, say, 10-fold crossvalidation (or a stratified validation, if for example the samples are paired). In my experience, this can lead to a disaster if generally your data set is small.

In a perfect world, you have also a validation set that you never use to train your model, not even in a CV setting. You keep it for the sole purpose of testing the final performance of a model before you send of the paper :-)


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