Predicting Conditional Quantiles via Reduction to Classification
This provides a novel method for quantile regression, which is important for uncertainty estimation in fields like finance and healthcare.
The paper tackles the problem of predicting conditional quantiles by reducing it to classification, showing that classifier regret bounds quantile regression regret under quantile loss. It achieves state-of-the-art performance on large real-world datasets.
We show how to reduce the process of predicting general order statistics (and the median in particular) to solving classification. The accompanying theoretical statement shows that the regret of the classifier bounds the regret of the quantile regression under a quantile loss. We also test this reduction empirically against existing quantile regression methods on large real-world datasets and discover that it provides state-of-the-art performance.