1. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
- Author
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Davide Chicco, Giuseppe Jurman, Chicco, D, and Jurman, G
- Subjects
Dataset imbalance ,lcsh:QH426-470 ,lcsh:Biotechnology ,Biostatistic ,Confusion matrice ,Binary number ,02 engineering and technology ,Biology ,Biostatistics ,Machine learning ,computer.software_genre ,Measure (mathematics) ,03 medical and health sciences ,Confusion matrices ,F1 score ,lcsh:TP248.13-248.65 ,0202 electrical engineering, electronic engineering, information engineering ,Genetics ,False positive paradox ,score ,Use case ,Correlation of Data ,Binary classification ,Accuracy ,030304 developmental biology ,0303 health sciences ,business.industry ,INF/01 - INFORMATICA ,Confusion matrix ,Computational Biology ,Genomics ,Matthews correlation coefficient ,lcsh:Genetics ,Data Interpretation, Statistical ,Genomic ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,computer ,Algorithms ,Biotechnology ,Research Article - Abstract
Background To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets. Results The Matthews correlation coefficient (MCC), instead, is a more reliable statistical rate which produces a high score only if the prediction obtained good results in all of the four confusion matrix categories (true positives, false negatives, true negatives, and false positives), proportionally both to the size of positive elements and the size of negative elements in the dataset. Conclusions In this article, we show how MCC produces a more informative and truthful score in evaluating binary classifications than accuracy and F1 score, by first explaining the mathematical properties, and then the asset of MCC in six synthetic use cases and in a real genomics scenario. We believe that the Matthews correlation coefficient should be preferred to accuracy and F1 score in evaluating binary classification tasks by all scientific communities.
- Published
- 2020