{"id":"W2999309192","doi":"10.1186/s12864-019-6413-7","title":"The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation","year":2020,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":5819,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tobacco Research Unit; Krembil Foundation","funders":"University of Toronto","keywords":"Binary classification; False positive paradox; Binary number; False positives and false negatives; Correlation; Confusion matrix; Artificial intelligence; Statistics; Pearson product-moment correlation coefficient; False positive rate; Confusion; Correlation coefficient; Matthews correlation coefficient; Computer science; Machine learning; Pattern recognition (psychology); Data mining; Mathematics; Support vector machine; Psychology; Arithmetic","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null}