Assigning Prediction Conditioned Well-Calibrated Probabilities to Set Predictions

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"

The data consists of 

• Python objects designed for thepaper which contain all models and data splits and cna be used to derive metrics with the code from github
• Training history for the models in those objects
• Simulated data whose distribution is described in the paper
• The MNIST dataset (LeCun, Y., Cortes, C., & Burges, C.J.C. (1998)), which can also be found at  http://yann.lecun.com/exdb/mnist/
• 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
• 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 Utility‑Driven Conformal Prediction: A Decision‑Aware Framework for Actionable Uncertainty Quantification.