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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? enter image description here

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