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RProgrammer (talk | contribs) By "recognition rate", specifically Sensitivity is meant. (At least that's what's consistent with the figures given) |
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== Table of confusion ==
In [[predictive analytics]], a '''table of confusion''' (sometimes also called a '''confusion matrix'''), is a table with two rows and two columns that reports the number of ''false positives'', ''false negatives'', ''true positives'', and ''true negatives''. This allows more detailed analysis than mere proportion of correct classifications (accuracy). Accuracy is not a reliable metric for the real performance of a classifier, because it will yield misleading results if the data set is unbalanced (that is, when the numbers of observations in different classes vary greatly). For example, if there were 95 cats and only 5 dogs in the data set, a particular classifier might classify all the observations as cats. The overall accuracy would be 95%, but in more detail the classifier would have a 100% recognition rate ([[sensitivity (test)|sensitivity]]) for the cat class but a 0% recognition rate for the dog class. F1 score is even more unreliable in such cases, and here would yield over 97.4%, whereas [[Informedness]] removes such bias and yields 0 as the probability of an informed decision for any form of guessing (here alway guessing cat).
Assuming the confusion matrix above, its corresponding table of confusion, for the cat class, would be:
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