Assessing Classification Accuracy with False Positives and False Negatives

I have been reading this forum but cannot find anything specific enough to address my problem.

I have classified disease in the below image (red spots), and verified disease by GPS (Red Circles).

I believe I have these three types of variables: • True Positive (TP): Correctly classified as the class of interest (GPS Spatially matches areas of Classified Disease) • False Positive (FP): Incorrectly classified as the class of interest (No GPS Data for Classified Disease) • False Negative (FN): Incorrectly classified as not the class of interest (GPS Data but no red spot/classification).

Based on this, what is a simple way I can derive single classification accuracy for my entire image- considering both False Positives and Negatives- and are there weights I can apply to improve my accuracy?

edited Feb 1, 2017 at 8:35

amoeba

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asked Jan 4, 2017 at 13:58

Alexander

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$\begingroup$ You seem to be discarding a large amount of information just to be able to do classification, as opposed to using probabilistic thinking which allows for gray zones and much more. $\endgroup$
– Frank Harrell
Jan 4, 2017 at 14:16
$\begingroup$ Hi Frank! It may seem that way- but I am showing just a small portion of an image here- and all I want to know is how accurately my classification technique can detect known areas of disease verified by GPS. $\endgroup$
– Alexander
Jan 4, 2017 at 14:26
1

$\begingroup$ The fact that you are using a classification technique is part of the problem unless the problem is deterministic, e.g., no unit-to-unit variation, no measurement error. $\endgroup$
– Frank Harrell
Jan 4, 2017 at 14:31
$\begingroup$ There are many relevant posts, search this site for "score function". Some examples: stats.stackexchange.com/questions/95250/… stats.stackexchange.com/questions/127042/… stats.stackexchange.com/questions/104500/… stats.stackexchange.com/questions/208529/… $\endgroup$
– kjetil b halvorsen ♦
Jan 4, 2017 at 15:41

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