title | booktitle | year | volume | series | month | publisher | url | abstract | layout | issn | id | tex_title | firstpage | lastpage | page | order | cycles | bibtex_editor | editor | bibtex_author | author | date | address | container-title | genre | issued | extras | ||||||||||||||||||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Clustered Conformal Prediction for the Housing Market |
Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications |
2024 |
230 |
Proceedings of Machine Learning Research |
0 |
PMLR |
Conformal prediction (CP) is a framework for constructing confidence sets around predictions from machine learning models with finite sample guarantees with few assumptions on both the prediction model and the data. In practice, the construction of CP sets typically relies on quantile estimates from an empirical distribution of non-conformity scores. When the data set consists of predefined, non-overlapping classes such as geographical regions, a common technique for improving the confidence sets is to calculate a different quantile for each class. However, the classwise quantile estimate suffers from high variance when the number of observations in each class is low. To circumvent this, one can share calibration data between classes with similar empirical distributions of non-conformity scores to reduce the variance of the quantile estimate. We study this approach for the application of house price prediction in the Norwegian housing market, where |
inproceedings |
2640-3498 |
hjort24a |
Clustered Conformal Prediction for the Housing Market |
366 |
386 |
366-386 |
366 |
false |
Vantini, Simone and Fontana, Matteo and Solari, Aldo and Bostr\"{o}m, Henrik and Carlsson, Lars |
|
Hjort, Anders and Williams, Jonathan P. and Pensar, Johan |
|
2024-09-10 |
Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications |
inproceedings |
|