Conformal Prediction Intervals with TabPFN for Tabular Data in Python and R
Researchers demonstrate the integration of conformal prediction intervals with TabPFN, a pretrained transformer model for tabular data, using both Python and R. The approach provides statistically valid coverage intervals for model predictions without assumptions about the data distribution, yielding robust performance on the diabetes dataset.