We are analyzing https://link.springer.com/article/10.1186/s12864-019-6413-7.

Title:
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation | BMC Genomics
Description:
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.
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Keywords {🔍}

google, scholar, mcc, score, accuracy, article, data, dataset, classification, correlation, case, coefficient, true, confusion, learning, correctly, negatives, binary, prediction, positives, instances, positive, negative, matthews, machine, performance, classifier, measures, results, imbalanced, measure, class, cas, matrix, cases, pubmed, patients, rate, table, false, additional, information, roc, interval, representing, file, evaluation, statistical, predict, ranking,

Topics {✒️}

text{tp}+\text{fn} text{tp}+\text{fp} text{tn}+\text{fn} text{tn}+\text{fp} text {ni}=\frac {1}{2} text categorization research f_{1}=\frac {2n^{+}}{2n^{+} evaluating text categorization textrm {nmcc}=\frac {1}{2} precision-recall-gain curves metagenomic profiles high-dimensional class-imbalanced data org/web/packages/plsgenomics/index precision–recall curve overcame text {ni} interacting enhancer–promoter pairs predict enhancer-promoters interactions cdot approximate rank-order clustering macro/micro averaging procedure k-category correlation coefficient biostatistics high-dimensional genomic data begin {array}{ll} begin {array}{ll}0 full size table multi-task deep learning micro-averaged evaluation measures precision-recall curves microarray-based predictive models micro/macro averaged f1 test inter-rater reliability pair precision/recall seqc/maqc-iii consortium full access davide chicco real-world model performance” eng adv technol author information authors designing multi-label classifiers precision-recall plot begin {array}{cc} begin {array}{cc}0 matthews correlation coefficient privacy choices/manage cookies low/high grade tumor receiver operating characteristic microarray quality control precision-recall roc enhancer–promoter interactions

Questions {❓}

A crucial issue naturally arises, concerning the outcome of a classification process: how to evaluate the classifier performance?
Given a clinical feature dataset of patients with cancer traits [1, 2], which patients will develop the tumor, and which will not?
How to evaluate performance of prediction methods?
When is “nearest neighbor” meaningful?

Schema {🗺️}

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         description: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. 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. 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.
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      headline:The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
      description: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. 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. 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.
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         Matthews correlation coefficient
         Binary classification
         F1 score
         Confusion matrices
         Machine learning
         Biostatistics
         Accuracy
         Dataset imbalance
         Genomics
         Life Sciences
         general
         Microarrays
         Proteomics
         Animal Genetics and Genomics
         Microbial Genetics and Genomics
         Plant Genetics and Genomics
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