  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.
