Conformal Prediction via Regression-as-Classification
Etash Guha Shlok Natarajan Thomas Möllenhoff Mohammad Emtiyaz Khan Eugene Ndiaye
Conformal prediction (CP) for regression can be challenging, especially when
the output distribution is heteroscedastic, multimodal, or skewed. Some of the
issues can be addressed by estimating a distribution over the output, but in
reality, such approaches can be sensitive to estimation error and yield
unstable intervals.~Here, we circumvent the challenges by converting regression
to a classification problem and then use CP for classification to obtain CP
sets for regression.~To preserve the ordering of the continuous-output space,
we design a new loss function and make necessary modifications to the CP
classification techniques.~Empirical results on many benchmarks shows that this
simple approach gives surprisingly good results on many practical problems.