Game Of Prediction
A game of prediction is a triple ⚠ $(\Omega,\Gamma,\lambda)$
, where ⚠ $\Omega$
is an outcome space, ⚠ $\Gamma$
is a decision space, and ⚠ $\lambda:\Omega\times\Gamma\to[-\infty,\infty]$
is the loss function. Important special cases are those of:
- point predictions, where
⚠ $\Gamma=\Omega$
; - probability predictions, where
⚠ $\Gamma$
is the set of all probability measures on⚠ $\Omega$
; if⚠ $\Omega$
is finite, the usual loss function is the log loss; - region predictions, where
⚠ $\Gamma$
is the set of all subsets of⚠ $\Omega$
; the usual loss function is
⚠ $\displaystyle{ \lambda(\omega,\gamma)= \begin{cases} 1 & \text{if } \omega\in\gamma\\ 0 & \text{otherwise}.\end{cases}}$
The case of region predictions (in particular, interval predictions) is the main object of study in conformal prediction but has never been studied in competitive on-line prediction. This is stated as open problem in the article competitive on-line interval prediction.