Added explanation for entropy threshold differences
Signed-off-by: Jim Martens <github@2martens.de>
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@ -964,9 +964,15 @@ has a high confidence in one class---including the background.
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However, the entropy plays a larger role for the Bayesian variants---as
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expected: the best performing thresholds are 1.0, 1.3, and 1.4 for micro averaging,
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and 1.5, 1.7, and 2.0 for macro averaging. In all of these cases the best
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threshold is not the largest threshold tested. A lower threshold likely
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eliminated some false positives from the result set. On the other hand a
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too low threshold likely eliminated true positives as well.
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threshold is not the largest threshold tested.
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This is caused by a simple phenomenon: at some point most or all true
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positives are in and a higher entropy threshold only adds more false
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positives. Such a behaviour is indicated by a stagnating recall for the
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higher entropy levels. For the low entropy thresholds, the low recall
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is dominating the \(F_1\) score, the sweet spot is somewhere in the
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middle. For macro averaging, it holds that a higher optimal entropy
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threshold indicates a worse performance.
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\subsection*{Non-Maximum Suppression and Top \(k\)}
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