Binary classifiers are accompanying us on a daily basis. Tests that detect disease, give us the answer: positive/negative, spam filters say spam/not spam, smartphones that authenticate us based on a face scan or fingerprint – make a known/unknown decision. The question: how to evaluate the efficiency of such a classifier does not seem extremely complicated. Just choose the one that will predict the most cases correctly. As many of us have already realized – the actual evaluation of a binary classifier requires somewhat more sophisticated means. But we’ll talk about that in a moment.
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This article explores the extension of well-known F1 score used for assessing the performance of binary classifiers. We propose the new metric using probabilistic interpretation of precision, recall, specifcity, and negative predictive value. We describe its properties and compare it to common metrics. Then we demonstrate its behavior in edge cases of the confusion matrix. Finally, the properties of the metric are tested on binary classifier trained on the real dataset.
Keywords: machine learning, binary classifier, F1 , MCC, precision, recall
Continue reading “Extending F1 metric, probabilistic approach”