Conformal Prediction: a Unified Review of Theory and New Challenges
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2020
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913216327581696 |
|---|---|
| author | Fontana, Matteo Zeni, Gianluca Vantini, Simone |
| author_facet | Fontana, Matteo Zeni, Gianluca Vantini, Simone |
| contents | In this work we provide a review of basic ideas and novel developments about Conformal Prediction -- an innovative distribution-free, non-parametric forecasting method, based on minimal assumptions -- that is able to yield in a very straightforward way predictions sets that are valid in a statistical sense also in in the finite sample case. The in-depth discussion provided in the paper covers the theoretical underpinnings of Conformal Prediction, and then proceeds to list the more advanced developments and adaptations of the original idea. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2005_07972 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Conformal Prediction: a Unified Review of Theory and New Challenges Fontana, Matteo Zeni, Gianluca Vantini, Simone Machine Learning Econometrics Methodology In this work we provide a review of basic ideas and novel developments about Conformal Prediction -- an innovative distribution-free, non-parametric forecasting method, based on minimal assumptions -- that is able to yield in a very straightforward way predictions sets that are valid in a statistical sense also in in the finite sample case. The in-depth discussion provided in the paper covers the theoretical underpinnings of Conformal Prediction, and then proceeds to list the more advanced developments and adaptations of the original idea. |
| title | Conformal Prediction: a Unified Review of Theory and New Challenges |
| topic | Machine Learning Econometrics Methodology |
| url | https://arxiv.org/abs/2005.07972 |