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arXiv · 1904.10679

Bayesian leave-one-out cross-validation for large data

Måns Magnusson·Michael Riis Andersen·Johan Jonasson·Aki Vehtari
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Abstract

Model inference, such as model comparison, model checking, and model selection, is an important part of model development. Leave-one-out cross-validation (LOO) is a general approach for assessing the generalizability of a model, but unfortunately, LOO does not scale well to large datasets. We propose a combination of using approximate inference techniques and probability-proportional-to-size-sampling (PPS) for fast LOO model evaluation for large datasets. We provide both theoretical and empirical results showing good properties for large data.

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Måns Magnusson, Michael Riis Andersen, Johan Jonasson, Aki Vehtari. 2019-04-24. Bayesian leave-one-out cross-validation for large data. https://arxiv.org/abs/1904.10679

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