Hierarchies of Calibration: Classification meets Regression
Concepts of calibration formalize the compatibility between probabilistic predictions and the respective outcomes. In a nutshell, the outcomes ought to be indistinguishable from random draws from the predictive distributions. In this paper, we review, extend, and bridge notions of calibration that have been proposed for classification and regression tasks. Particular emphasis is given to hierarchical relations between the various notions, as they apply to general real-valued data, continuous outcomes, count data, nominal classes, and binary outcomes. To highlight a number of contributions, we introduce the notion of modal calibration for nominal outcomes, we distinguish full, partial, and...
| Authors | Johannes Resin, Lu Yang, Tilmann Gneiting |
|---|---|
| Primary category | stat.ML |
| Published | 2 June 2026 |
| Last updated at source | 2 June 2026 |
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