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.

翻译：回归问题的保形预测（CP）可能具有挑战性，尤其是在输出分布存在异方差、多模态或偏态时。部分问题可以通过估计输出分布来解决，但在实际中，这类方法对估计误差敏感，可能产生不稳定的预测区间。本文通过将回归问题转化为分类问题，并利用分类任务的保形预测方法获得回归问题的保形预测集，从而规避了上述挑战。为保持连续输出空间的序结构，我们设计了一种新的损失函数，并对分类保形预测技术进行了必要调整。在多个基准测试上的实验结果表明，这种简单方法在众多实际问题中取得了令人惊讶的优异效果。