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Similarly, the statement "Sensitivity (TPR) is the proportion of people that are actually positive (TP) of all the people that tested positive (TP+FP)" should read " Sensitivity (TPR) is the proportion of people that are tested positive (TP) of all the people that are truly positive (TP+FN)". All the
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The following sentence 'With higher specificity, fewer healthy people are labeled as sick (or, in the factory case, the less money the factory loses by discarding good products instead of selling them)." again corresponds to TN/(TN+FP), where FP= healthy (Positive) people are labeled as sick (False
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This is a really poor article. Apart from the last section "Converting continuous values to binary" which is vaguely related, there's nothing in here about *binary classification*. How do you do binary classification? What are the established methods? Statistical methods? Machine learning methods?
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I agree about the discrepancy. The statement "Specificity (TNR) is the proportion of people that are actually negative (TN) of all the people that tested negative (TN+FN)" should read "Specificity (TNR) is the proportion of people that are tested negative (TN) of all the people who are actually
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This correction will be consistent with the follow-up statement 'As with sensitivity ... given that the patient is not sick', which are those people who are actually healthy, or (TN+FP), regardless of any test, and *not* those whose TEST results label them as not sick, or
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Why is this large section on sensitivity and specificity even in this article? Its totally unnecessary, if you wanted to know about sensitivity and specificity you'd go to the article about sensitivity and specificity.
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If sickness is the condition to be detected with least error possible, then 'Specifity' is a measure of how well healthy people are detected, and 'Sensitivity' is a measure of how well sick people are detected
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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.
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specificity is defined as TN/(TN+FP) and sensitivity is defined as TP/(TP+FN). In this article the definitions given are incorrect (specificity = TN/(TN+FN), sensitivity = TP/(TP+FP)).
423:. 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...
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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
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Should the article not discuss trade offs by varying the discrimination threshold. eg the medical test example may choose some specific value of a
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I think this graphics is extremely confusing. Even though it seems cute and simple, it has so many annotated arrows compared to
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as a threshold for making a positive prediction ? Varying the threshold trades off false positives against false negatives. -
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The article is supposed to be about binary classification not hypothesis testing and sensitivity and sensitivity!
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Thus, the number of true positives, false negatives, true negatives, and false positives add up to 100% of the set.
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English is not my native language, so I'm very grateful for help with grammar and spelling. //
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is a positive... one that has been accurately predicted as positive. A
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In my opinion TP and FN add up to 100% as FP and TN do as well. //--
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