From fdcf1af62e77902f425166f1bcc3af2d76a7c837 Mon Sep 17 00:00:00 2001 From: Jim Martens Date: Wed, 25 Sep 2019 12:34:20 +0200 Subject: [PATCH] Added explanation for entropy threshold differences Signed-off-by: Jim Martens --- body.tex | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/body.tex b/body.tex index 09c6cab..c28b9b5 100644 --- a/body.tex +++ b/body.tex @@ -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 \(k\)}