Bayesian leave-one-out cross-validation for large data
Machine Learning
2020-08-12 v1 Machine Learning
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.
Cite
@article{arxiv.1904.10679,
title = {Bayesian leave-one-out cross-validation for large data},
author = {Måns Magnusson and Michael Riis Andersen and Johan Jonasson and Aki Vehtari},
journal= {arXiv preprint arXiv:1904.10679},
year = {2020}
}
Comments
Accepted to ICML 2019. This version is the submitted paper