The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Abstract
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 F 1 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 F 1 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 F 1 score in evaluating binary classification tasks by all scientific communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- BMC Genomics
- Topic
- Imbalanced Data Classification Techniques
- Field
- Computer Science
- Canadian institutions
- Ontario Tobacco Research UnitKrembil Foundation
- Funders
- University of Toronto
- Keywords
- Binary classificationFalse positive paradoxBinary numberFalse positives and false negativesCorrelationConfusion matrixArtificial intelligenceStatisticsPearson product-moment correlation coefficientFalse positive rateConfusionCorrelation coefficientMatthews correlation coefficientComputer scienceMachine learningPattern recognition (psychology)Data miningMathematicsSupport vector machinePsychologyArithmetic
- Has abstract in OpenAlex
- yes