Valid Inferential Models for Prediction in Supervised Learning Problems
Abstract
Prediction, where observed data is used to quantify uncertainty about a future observation, is a fundamental problem in statistics. Prediction sets with coverage probability guarantees are a common solution, but these do not provide probabilistic uncertainty quantification in the sense of assigning beliefs to relevant assertions about the future observable. Alternatively, we recommend the use of a probabilistic predictor, a fully-specified (imprecise) probability distribution for the to-be-predicted observation given the observed data. It is essential that the probabilistic predictor is reliable or valid in some sense, and here we offer a notion of validity and explore its implications. We also provide a general inferential model construction that yields a provably valid probabilistic predictor, with illustrations in regression and classification.
Cite
Text
Cella and Martin. "Valid Inferential Models for Prediction in Supervised Learning Problems." Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications, 2021.Markdown
[Cella and Martin. "Valid Inferential Models for Prediction in Supervised Learning Problems." Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications, 2021.](https://mlanthology.org/isipta/2021/cella2021isipta-valid/)BibTeX
@inproceedings{cella2021isipta-valid,
title = {{Valid Inferential Models for Prediction in Supervised Learning Problems}},
author = {Cella, Leonardo and Martin, Ryan},
booktitle = {Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications},
year = {2021},
pages = {72-82},
volume = {147},
url = {https://mlanthology.org/isipta/2021/cella2021isipta-valid/}
}