# A relation between log-likelihood and cross-validation log-scores

arXiv: https://arxiv.org/abs/1908.08741 · HTML: https://mlsanity.com/paper/1908.08741/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2019.

## Built on by (most cited first, 1 of 1)

- [Don't guess what's true: choose what's optimal. A probability transducer for machine-learning classifiers](https://mlsanity.com/paper/2302.10578/) (2023)

## Abstract (arXiv)

It is shown that the log-likelihood of a hypothesis or model given some data is equivalent to an average of all leave-one-out cross-validation log-scores that can be calculated from all subsets of the data. This relation can be generalized to any k-fold cross-validation log-scores.