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Calibration: Difference between revisions
Update description of calibration to reflect different forms of calibration
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Calibration refers to the propensity of a forecaster's forecasts to occur at the approximate frequency of their prediction. For example, a forecaster who forecasts 10 events at 40% each and 4 of those events ultimately occur exhibits good calibration. If 3 or 5 of these events occur, the forecaster may still be exhibiting reasonable calibration and merely have been slightly unlucky. Greater deviations indicate that the forecaster is less calibrated. Accurately judging a forecaster's calibration requires the resolution of many forecasts across the spectrum of probabilities.▼
Calibration describes the statistical consistency between a probabilistic forecast and the observed values <ref>[https://psycnet.apa.org/record/2011-26535-000] Gneiting, T., Balabdaoui, F. and Raftery, A.E. (2007), Probabilistic forecasts, calibration and sharpness. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69: 243-268. <nowiki>https://doi.org/10.1111/j.1467-9868.2007.00587.x</nowiki></ref>. One can distinguish different forms of calibration, most importantly probabilistic calibration, marginal calibration and exceedance calibration. Among these, probabilistic calibration is by far the most used.
== Probabilistic calibration ==
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== Assessing Calibration ==
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