Talk:Binary classification

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English is not my native language, so I'm very grateful for help with grammar and spelling. // Janka

This sentence in "Evaluation of binary classifiers" might not be correct:

Thus, the number of true positives, false negatives, true negatives, and false positives add up to 100% of the set.

In my opinion TP and FN add up to 100% as FP and TN do as well. //--83.171.184.232 (talk) 22:33, 6 January 2008 (UTC)

Well a TP is a positive... one that has been accurately predicted as positive. A FN is a positive... one that has inaccurately been predicted as a negative. Together, they make up the set of positive elements. This, however, is only 100% if there are no negative elements.

Another way of looking at this is as follows: imagine you are doing pregnancy tests. The set of women who are pregnant and are tested as such would be your TP, and then women who are pregnant and who did not show up as such would be your FN. However, this would not necessarily make up 100% of the women tested, as there could be women who are indeed not pregnant. This would be your set of negative elements, and the accuracy of the tests would determine whether they are TN or FP. The total set of women who are pregnant (the positives, regardless of prediction) and those who aren't (the negatives) would be 100% of the women. Hope this helps... WDavis1911 (talk) 16:54, 10 June 2008 (UTC)