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| Format: | Recurso digital |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.19681063 |
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Table of Contents:
- <p>This is the data and class-objects that are needed to reproduce results from the paper "Assigning Prediction Conditioned Well-Calibrated Probabilities to Set Predictions"</p> <p> </p> <p>The data consists of </p> <ul> <li>Python objects designed for thepaper which contain all models and data splits and cna be used to derive metrics with the code from github</li> <li>Training history for the models in those objects</li> <li>Simulated data whose distribution is described in the paper</li> <li>The MNIST dataset (LeCun, Y., Cortes, C., & Burges, C.J.C. (1998)), which can also be found at http://yann.lecun.com/exdb/mnist/</li> <li>The CIFAR-10 dataset (Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.(2009)), which can also be found at https://www.cs.toronto.edu/~kriz/cifar.html</li> <li>Labels, hierarchies and prediction probabilties on the clean fitzpatrick dataset originally generated by Cortes‑Gomez, S., Patiño, C. M., Byun, Y., Wu, Z. S., Horvitz, E., & Wilder, B. (2025) for the paper <em>Utility‑Driven Conformal Prediction: A Decision‑Aware Framework for Actionable Uncertainty Quantification.</em></li> </ul>