Heteroskedastic conformal regression
Nicolas Dewolf Bernard De Baets Willem Waegeman
Conformal prediction, and split conformal prediction as a specific
implementation, offer a distribution-free approach to estimating prediction
intervals with statistical guarantees. Recent work has shown that split
conformal prediction can produce state-of-the-art prediction intervals when
focusing on marginal coverage, i.e., on a calibration dataset the method
produces on average prediction intervals that contain the ground truth with a
predefined coverage level. However, such intervals are often not adaptive,
which can be problematic for regression problems with heteroskedastic noise.
This paper tries to shed new light on how adaptive prediction intervals can be
constructed using methods such as normalized and Mondrian conformal prediction.
We present theoretical and experimental results in which these methods are
investigated in a systematic way.