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Added explanation for entropy threshold differences 

Signed-off-by: Jim Martens <github@2martens.de>
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Jim Martens 2019-09-25 12:34:20 +02:00
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@ -964,9 +964,15 @@ has a high confidence in one class---including the background.
However, the entropy plays a larger role for the Bayesian variants---as
expected: the best performing thresholds are 1.0, 1.3, and 1.4 for micro averaging,
and 1.5, 1.7, and 2.0 for macro averaging. In all of these cases the best
threshold is not the largest threshold tested. A lower threshold likely
eliminated some false positives from the result set. On the other hand a
too low threshold likely eliminated true positives as well.
threshold is not the largest threshold tested.
This is caused by a simple phenomenon: at some point most or all true
positives are in and a higher entropy threshold only adds more false
positives. Such a behaviour is indicated by a stagnating recall for the
higher entropy levels. For the low entropy thresholds, the low recall
is dominating the \(F_1\) score, the sweet spot is somewhere in the
middle. For macro averaging, it holds that a higher optimal entropy
threshold indicates a worse performance.
\subsection*{Non-Maximum Suppression and Top \(k\)}