Conformalized TabPFN: Prediction Intervals for a Pretrained Transformer for Tabular Data in Python and R

A tutorial demonstrating how to generate statistically valid prediction intervals for tabular regression by combining TabPFN (a pretrained transformer for tabular data) with nnetsauce's Split Conformal Prediction wrapper. The full pipeline is shown in both Python and R (via reticulate), using the sklearn diabetes dataset. Both implementations achieve a 96.7% coverage rate at a nominal 95% confidence level, with complete code examples including data loading, model fitting, conformal wrapping, and visualization.