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F1… and I’m not talking about running – Healthcare Economist


Most people know the meaning of accuracy. Find out how many estimates you have made and how many reports you have made that are accurate. In the case of a diagnostic test, this is just the number of correct answers (true facts including actual errors) shared by the number of answers (true facts + false + true errors + false errors). In summary, let’s show the number 4 in denominator TP, FP, TN and FN.

However, there are other metrics that one can use. Recall — also known as sensitivity — looks at the best part of the case your test may take. These are read as TP / (TP + FN). Another practical and accurate metric – also known as a positive predictive value (PPV) that compares the amount of positive results your test obtained is actually positive. These are read as TP / (TP + FP).

Another way to measure test accuracy is F-score or F-measure. For example, a F1 a score is the way to link accuracy and memory, and ranges from 1.0 (good accuracy) to 0.0 (not very accurate). F-score is often used to measure machine learning accuracy as well as natural language prediction. It reads as follows:

An additional method of F-score is to use Fb, which uses the positive β factor, which remembers (ie, sensitivity) is considered nthawi times to be the most important (ie, PPV). Fb can be read as follows:

Another correct method is known as correct accuracy. Proper accuracy is simply the result of the conviction and certainty of: [(TP)/(TP+FN) + (TN)/(TN+FP)]/ 2.

Consider the example below where the test has a very good knowledge (0.934) but not very accurate (0.556). In this example, however, most people assume they have hope, so its accuracy is good (0.900). However, the correct accuracy takes into account the accuracy of the number of people who may be the richest between good and bad cases and finds that the correct accuracy is only 0.745.



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