Editing Confusion matrix (section)

==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 ''true positives'', ''false negatives'', ''false positives'', and ''true negatives''. This allows more detailed analysis than simply observing the proportion of correct classifications (accuracy). Accuracy 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 cancer samples and only 5 non-cancer samples in the data, a particular classifier might classify all the observations as having cancer. The overall accuracy would be 95%, but in more detail the classifier would have a 100% recognition rate ([[sensitivity (test)|sensitivity]]) for the cancer class but a 0% recognition rate for the non-cancer 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 always guessing cancer).

According to Davide Chicco and Giuseppe Jurman, the most informative metric to evaluate a confusion matrix is the [[Matthews correlation coefficient|Matthews correlation coefficient (MCC)]].<ref>{{cite journal |vauthors = Chicco D, Jurman G |title = The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation |journal = BMC Genomics |volume = 21 |issue = 1 |date = January 2020 |page = 6-1–6-13 |pmid = 31898477 |doi = 10.1186/s12864-019-6413-7 |pmc = 6941312 |doi-access = free }}</ref>

Other metrics can be included in a confusion matrix, each of them having their significance and use.

{{diagnostic testing diagram}}