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The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
Open accessAbstract
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.
Overview
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TL;DR
The paper argues that the Matthews correlation coefficient (MCC) is a more reliable metric than accuracy and F1 score for evaluating binary classifications, especially on imbalanced datasets, because MCC requires good performance on both positive and negative classes. It supports this through mathematical analysis, six synthetic use cases, and a real colon cancer gene expression experiment.
Key findings
- In six synthetic use cases, MCC was the only metric that correctly indicated the prediction problem in all six. For Use case A1 (91 positives, 9 negatives; TP=90, FN=1, TN=0, FP=9), accuracy=0.90 and F1=0.95, but MCC=−0.03. For Use case C2 (11 positives, 89 negatives; TP=2, FN=9, TN=88, FP=1), accuracy=0.90, F1=0.29, and MCC=+0.31.
- Correlation analysis over all possible confusion matrices showed MCC and accuracy were strongly correlated, while F1 correlated less than 0.8 with the other two measures. For N=1000, PCC(MCC,F1)=0.775982, PCC(MCC,accuracy)=0.921030, and PCC(accuracy,F1)=0.778652.
- In the colon cancer gene expression dataset (2,000 gene probesets, 62 patients: 22 healthy controls and 40 colon cancer; 35.48% negatives, 64.52% positives), rankings differed by metric. MCC ranked gradient boosting top (MCC=+0.55, TP rate=0.85, TN rate=0.69), while F1 and accuracy ranked k-NN top (F1=0.87, accuracy=0.80, TP rate=0.92, TN rate=0.52). Radial SVM had F1=0.75 and accuracy=0.67 but TN rate=0.40 and MCC=+0.29.
- MCC produces a high score only if the classifier correctly predicts the majority of positive and negative instances; F1 is independent of true negatives and is not symmetric for class swapping, while accuracy can be overoptimistic on imbalanced datasets.
Methods
- The paper defines accuracy, MCC, and F1 score mathematically, explores their relationships using Pearson correlation coefficient over all possible confusion matrices for N≥10 up to 1000, and plots all 21,084,251 possible confusion matrices for a 500-sample dataset on the MCC/F1 plane.
- Six synthetic use cases vary class balance and imbalance with specified confusion matrices to compare metric behavior.
- The real genomics experiment uses colon cancer gene expression data from Alon et al. via the plsgenomics R package, applying gradient boosting, decision tree, k-nearest neighbors, linear SVM, and radial SVM. Training/test splits were 80/20 or 60/20/20 with validation for hyperparameter grid search; experiments were repeated ten times and averaged for MCC, F1, accuracy, TP rate, and TN rate.
Limitations
- The paper states that additional comparisons with other rates such as Cohen’s Kappa, Cramér’s V, and K measure would have provided further information, but the authors focused on accuracy and F1 because they are more commonly used in biomedical machine learning studies.
- The paper notes that MCC can be undefined when a whole row or column of the confusion matrix is zero, and handles these cases via an approximation using an arbitrarily small value ε; it also cites situations where MCC can display large fluctuations due to imbalanced outcomes.
- Evident from the text: the real-world validation uses a single colon cancer gene expression dataset, the six use cases are synthetic, and Table 5 excludes standard deviations to keep the table simple.
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![Fig. 2 Use case A1 — Positively imbalanced dataset. a Barplot representing accuracy, F1, and normalized Matthews correlation coefficient (normMCC = (MCC + 1) / 2), all in the [0, 1] interval, where 0 is the worst possible score and 1 is the best possible score, applied to the Use case A1 positively imbalanced dataset. b Pie chart representing the amounts of true positives (TP), false negatives (FN), true negatives (TN), and false positives (FP). c Pie chart representing the dataset balance, as the amounts of positive data instances and negative data instances](/paper-figures/988371a13eb649a5bff767f781052a4f/the-advantages-of-the-matthews-correlation-coefficient-mcc-over-f1-score-and-figure-2.jpg)
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29 Own interpretation · 28 Own result · 15 Not checked yet · 13 No basis given
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In the paper
“A potential problem with MCC lies in the fact that MCC is undefined when a whole row or column of M is zero, as it happens in the previously cited case of the trivial majority classifier.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Notation and mathematical foundations
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“Second, F1 score is independent from the number of samples correctly classified as negative.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
plain- Asserted: passes — The authors put it forward as true.
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“she/he would notice good results (F1 score = 0.75 and accuracy = 0.67)”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Genomics scenario: colon cancer gene expression
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“For the Use case A1 (negatively imbalanced dataset), the machine learning classifier was unable to correctly predict negative data instances, and it therefore produced confusion matrices featuring few true negatives (TN).”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
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“First, the top classifier changes when we consider the ranking based on MCC, F1 score, or accuracy.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Genomics scenario: colon cancer gene expression
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“MCC = +0.31 in the [–1, +1] interval”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
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In the paper
“In the Use case B1 (balanced dataset), the machine learning method was unable to correctly predict negative data instances, and therefore produced a confusion matrix featuring few true negatives (TN).”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
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“Finally, in the last Use case C2 (positively imbalanced dataset), the prediction technique failed in predicting negative elements, and therefore its confusion matrix showed a low percentage of true negatives.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
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“F1 score = 0.29 in the [0, 1] interval”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
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“the radial SVM has performed poorly on the true negatives (TN rate = 0.40), by correctly predicting less than half patients.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Genomics scenario: colon cancer gene expression
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“Here, accuracy showed a high value: 0.90 in the [0, 1] interval.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
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“In this case, all three statistical rates showed a low score which emphasized the deficiency in the prediction process (accuracy = 0.24, F1 score = 0.12, and MCC = −0.24).”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
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“It ranges in the interval [−1,+1], with extreme values –1 and +1 reached in case of perfect misclassification and perfect classification, respectively, while MCC=0 is the expected value for the coin tossing classifier.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Notation and mathematical foundations
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“F1 ranges in [0,1], where the minimum is reached for TP=0, that is, when all the positive samples are misclassified, and the maximum for FN=FP=0, that is for perfect classification.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Notation and mathematical foundations
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“Like for the Use case A2, accuracy, F1 and MCC show average or low result scores (accuracy = 0.56, F1 score = 0.31, and MCC = +0.17), correctly informing you about the non-optimal performance of the prediction method (Additional file 3a).”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
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“If M has only one non-zero entry, this means that all samples in the dataset belong to one class, and they are either all correctly (for TP≠0 or TN≠0) or incorrectly (for FP≠0 or FN≠0) classified. In this situations, MCC=1 for the former case and MCC=−1 for the latter case.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Notation and mathematical foundations
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
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“If the dataset is highly unbalanced, ni≈1, and thus the accuracy measure gives an unreliable estimation of the goodness of the classifier.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Notation and mathematical foundations
plaindirect- Asserted: passes — The authors put it forward as true.
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“Checking only F1, one would read a good value (0.66 in the [0, 1] interval), and would be overall satisfied about the prediction (Additional file 2a).”
- Also stated in: § Use cases · hedged
“The Matthews correlation coefficient, instead, by showing a score close to random guessing (+0.07 in the [–1, +1] interval) would be able to inform that the machine learning method has been on the wrong track. Also, it is worth noticing that accuracy would provide with an informative result in this case (0.52 in the [0, 1] interval).”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
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- Also stated in: § Use cases · hedged
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“However, accuracy and of F1 showed high values in this case: accuracy = 0.90 and F1 score = 0.95, both close to the best possible value 1.00 in the [0, 1] interval (Fig. 2a).”
- Also stated in: § Use cases · hedged
“Instead, if one decided to take advantage of the Matthews correlation coefficient in the Use case A1, he/she would notice the resulting MCC = –0.03 (Fig. 2a).”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
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- Also stated in: § Use cases · hedged
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“Similar to the Use case A2 and B2, all three statistical scores generated low results that reflect the mediocre quality of the prediction: F1 score = 0.17 and accuracy = 0.10 in the [0, 1] interval, and MCC = −0.19 in the [–1, +1] interval (Additional file 4a).”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
plainSingle case- Asserted: passes — The authors put it forward as true.
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“On the other hand, for a given value MCC=μ, the F1 score can range in [0,μ+1] if μ≤0 and in [μ,1] if μ>0, so that the width of the range is 1−|μ|, that is, it depends on the MCC value μ.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Relationship between measures
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“The two measures are reasonably concordant, but the scatterplot cloud is wide, implying that for each value of F1 score there is a corresponding range of values of MCC and vice versa, although with different width.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Relationship between measures
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Overall, accuracy, F1, and MCC show reliable concordant scores for predictions that correctly classify both positives and negatives (having therefore many TP and TN), and for predictions that incorrectly classify both positives and negatives (having therefore few TP and TN); however, these measures show discordant behaviors when the prediction performs well just with one of the two binary classes.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Relationship between measures
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“First, F1 varies for class swapping, while MCC is invariant if the positive class is renamed negative and vice versa.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Interestingly, the correlation grows with N, but the increments are limited.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Relationship between measures
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“With these positions MCC is now defined for all confusion matrices M. As a consequences, MCC=0 for the trivial majority classifier, and 0 is also the expected value for the coin tossing classifier.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Notation and mathematical foundations
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In the paper
“MCC and accuracy resulted strongly correlated, while the Pearson coefficient is less than 0.8 for the correlation of F1 with the other two measures (Table 3).”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Relationship between measures
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“In fact, for any value F1=ϕ, the MCC varies approximately between [ϕ−1,ϕ], so that the width of the variability range is 1, independent from the value of ϕ.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Relationship between measures
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“The micro/macro averaged F1 is invariant for class swapping and its behaviour is more similar to MCC.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Here, accuracy showed a high value: 0.90 in the [0, 1] interval.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“linear SVM is ranked forth in the MCC ranking and in the accuracy ranking, but ranked second in the F1 score ranking.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Genomics scenario: colon cancer gene expression
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In the paper
“k-NN, that is the top performing method both in the F1 score ranking and in the accuracy ranking, instead, obtained an excellent score for recall (TP rate = 0.92) but just sufficient on the specificity (TN rate = 0.52).”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Genomics scenario: colon cancer gene expression
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“However, ROC AUC presents several flaws [92], and it is sensitive to class imbalance [93].”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
What the sources say
- Not checked yetcites [92] Lobo JM, Jiménez-Valverde A, Real R. AUC: a misleading measure of the performance of predictive distribution models. Glob Ecol Biogeogr. 2008; 17(2):145–51. source
- Not checked yetcites [93] Hanczar B, Hua J, Sima C, Weinstein J, Bittner M, Dougherty ER. Small-sample precision of ROC-related estimates. Bioinformatics. 2010; 26(6):822–30. source
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In the paper
“Once again, this score would hide the truth: the classification algorithm has performed poorly on the negative subset.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
hedgeddirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one. The quote only partly states this claim.
None of the sources cited for this claim have been checked yet.
In the paper
“The effectiveness of MCC has been shown in other scientific fields as well [43, 44].”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
What the sources say
- Not checked yetcites [43] Liu Y, Cheng J, Yan C, Wu X, Chen F. Research on the Matthews correlation coefficients metrics of personalized recommendation algorithm evaluation. Int J Hybrid Informa Technol. 2015; 8(1):163–72. source
- Not checked yetcites [44] Naulaerts S, Dang CC, Ballester PJ. Precision and recall oncology: combining multiple gene mutations for improved identification of drug-sensitive tumours. Oncotarget. 2017; 8(57):97025. source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Even if less common than the ROC curve, several scientists consider the PR curve more informative than the ROC curve, especially on imbalanced biological and medical datasets [48, 97, 98].”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
What the sources say
- Not checked yetcites [48] Saito T, Rehmsmeier M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE. 2015; 10(3):0118432. source
- Not checked yetcites [97] Chicco D. Ten quick tips for machine learning in computational biology. BioData Min. 2017; 10(35):1–17.
- Not checked yetcites [98] Ozenne B, Subtil F, Maucort-Boulch D. The precision–recall curve overcame the optimism of the receiver operating characteristic curve in rare diseases. J Clin Epidemiol. 2015; 68(8):855–9. source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“MCC was re-proposed by Baldi and colleagues [39] in 2000 as a standard performance metric for machine learning with a natural extension to the multiclass case [40].”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
What the sources say
- Not checked yetcites [39] Baldi P, Brunak S, Chauvin Y, Andersen CA, Nielsen H. Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics. 2000; 16(5):412–24. source
- Not checked yetcites [40] Gorodkin J. Comparing two K-category assignments by a K-category correlation coefficient. Comput Biol Chem. 2004; 28(5–6):367–74. source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“For example, when Whalen and colleagues released TargetFinder, a tool to predict enhancer-promoters interactions in genomics, they showed its results measured only by F1 score [13], making it impossible to detect the actual true positive rate and true negative rate of their tests [69].”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
What the sources say
- Not checked yetcites [13] Whalen S, Truty RM, Pollard KS. Enhancer–promoter interactions are encoded by complex genomic signatures on looping chromatin. Nat Genet. 2016; 48(5):488. source
- Not checked yetcites [69] Xi W, Beer MA. Local epigenomic state cannot discriminate interacting and non-interacting enhancer–promoter pairs with high accuracy. PLoS Comput Biol. 2018; 14(12):1006625. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Although being widely acknowledged as a reliable metric, there are situations - albeit extreme - where either MCC cannot be defined or it displays large fluctuations [45], due to imbalanced outcomes in the classification.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
What the sources say
- Not checked yetcites [45] Brown JB. Classifiers and their metrics quantified. Mol Inform. 2018; 37:1700127. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“An effective solution overcoming the class imbalance issue comes from the Matthews correlation coefficient (MCC), a special case of the ϕ phi coefficient [36].”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
What the sources say
- Not checked yetcites [36] Guilford JP. Psychometric Methods. New York City: McGraw-Hill; 1954.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“for instance, the Food and Drug Administration (FDA) agency of the USA employed the MCC as the main evaluation measure in the MicroArray II / Sequencing Quality Control (MAQC/SEQC) projects [41, 42].”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
What the sources say
- Not checked yetcites [41] The MicroArray Quality Control (MAQC) Consortium. The MAQC-II Project: a comprehensive study of common practices for the development and validation of microarray-based predictive models. Nat Biotechnol. 2010; 28(8):827–38. source
- Not checked yetcites [42] The SEQC/MAQC-III Consortium. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequence Quality Control consortium. Nat Biotechnol. 2014; 32:903–14. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“However, this procedure is biased [65], and it is still far from being accepted as a standard practice by the community.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
What the sources say
- Not checked yetcites [65] Van Asch V. Macro-and micro-averaged evaluation measures. Technical report. 2013:1–27.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In this case, the algorithm showed its ability to predict the positive data instances (90 sick patients out of 91 were correctly predicted), but it also displayed its lack of talent in identifying healthy controls (only 1 healthy individual out of 9 was correctly recognized) (Fig. 2b). Therefore, the overall performance should be judged poor.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Similar to the Use case A1 and B1, the method was able to correctly identify multiple negative data instances (88 healthy patients out of 89), but unable to correctly predict most of sick patients (only 2 true positives out of 11 possible elements).”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
plainn = 88- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Originally developed by Matthews in 1975 for comparison of chemical structures [38]”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
What the sources say
- Not checked yetcites [38] Matthews BW. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta (BBA) Protein Struct. 1975; 405(2):442–51. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Despite its popularity, in the learning context there are a number of issues causing the kappa measure to produce unreliable results (for instance, its high sensitivity to the distribution of the marginal totals [73–75])”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Background
What the sources say
- Not checked yetcites [73] Flight L, Julious SA. The disagreeable behaviour of the kappa statistic. Pharm Stat. 2015; 14:74–8. source
- Not checked yetcites [74] Powers DMW. The problem with Kappa. In: Proceedings of EACL 2012 – the 13th Conference of the European Chapter of the Association for Computational Linguistics. Avignon: ACL: 2012. p. 345–55.
- Not checked yetcites [75] Delgado R, Tibau X-A. Why Cohen’s Kappa should be avoided as performance measure in classification. PloS ONE. 2019; 14(9):0222916.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In fact, when a prediction displays many true positives but few true negatives (or many true negatives but few true positives) we will show that F1 and accuracy can provide misleading information, while MCC always generates results that reflect the overall prediction issues.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Relationship between measures
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These results show that, while accuracy and F1 score often generate high scores that do not inform the user about ongoing prediction issues, the MCC is a robust, useful, reliable, truthful statistical measure able to correctly reflect the deficiency of any prediction in any dataset.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
boosteddirectn = 6- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one. The quote only partly states this claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Here accuracy again generated overoptimistic, misleading, and inflated high results”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The F1 score ranking and the accuracy ranking, in conclusion, are hiding this important flaw of the top classifier: k-NN was unable top correctly predict a high percentage of patients.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Genomics scenario: colon cancer gene expression
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There, accuracy and F1 generated overoptimistic and inflated results”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Accuracy and F1 score, although popular, can generate misleading results on imbalanced datasets, because they fail to consider the ratio between positive and negative elements.”
- Also stated in: § Conclusions · plain
“F1 and accuracy, instead, generate reliable results only when applied to balanced datasets, and produce misleading results when applied to imbalanced cases.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Conclusions
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § Conclusions · plain
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“With its low value (MCC = +0.29), the MCC informs the readers about the poor general outcome of the radial SVM, while the accuracy and F1 score show misleading values.”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Genomics scenario: colon cancer gene expression
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The dataset contains 2,000 gene probsets for 62 patients, of which 22 are healthy controls and 40 have colon cancer (35.48% negatives and 64.52% positives) [106].”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Genomics scenario: colon cancer gene expression
What the sources say
- Not checked yetcites [106] Alon U, Barkai N, Notterman DA, Gish K, Ybarra S, Mack D, Levine AJ. Data pertaining to the article ‘Broad patterns of gene expression revealed by clustering of tumor and normal colon tissues probed by oligonucleotide arrays’. 2000. http://genomics-pubs.princeton.edu/oncology/affydata/index.html. Accessed 10 Dec 2019.
plainn = 62- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
Accuracy and F1 score are the most common metrics used for binary classification in machine learning.
“We decided to focus on accuracy and F1 score because they are the most common metrics used for binary classification in machine learning.”
The paper relies on this without support.
The synthetic use cases are realistic representations of binary classification scenarios.
“here we describe some synthetic, realistic scenarios where MCC results are more informative and truthful than the other two measures analyzed.”
The paper relies on this without support.
Accuracy and F1 score are more commonly used in machine learning studies related to biomedical applications than other rates.
“because accuracy and F1 score are more commonly used in machine learning studies related to biomedical applications.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“MCC is a more reliable metric, whereby a high score is assigned only if the prediction was correct for the four base categories (TP, FP, TN and FP), taking into account the size of P and N [85].”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [85]
In the citing paper
“since it has been shown to present a balanced score when dealing with imbalanced data [11, 6] as is our case with fewer manipulated images.”
From BioFors: A Large Biomedical Image Forensics Dataset (2021) · cites this paper as [11]
In the citing paper
“(3) the absolute value of F1 changes by simply switching the outcome labels (ie, when 1 becomes 0 and 0 becomes 1).”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [77]
In the citing paper
“(2) F1 has no intuitive interpretation”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [77]
In the citing paper
“(1) F1 ignores true negatives”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [77]
In the citing paper
“F1 and MCC were introduced to address the challenges caused by class imbalance, where classification accuracy can be inflated by classifying all patients as low risk when the event is rare.”
From Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance (2025) · cites this paper as [77]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“Here, accuracy again generated an overoptimistic inflated score”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“all three statistical scores generated low results that reflect the mediocre quality of the prediction: F1 score = 0.17 and accuracy = 0.10 in the [0, 1] interval”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“the prediction technique failed in predicting negative elements, and therefore its confusion matrix showed a low percentage of true negatives”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“the method was able to correctly identify multiple negative data instances (88 healthy patients out of 89), but unable to correctly predict most of sick patients (only 2 true positives out of 11 possible elements)”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“Even if accuracy showed an excessively high result score”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“all three statistical rates showed a low score which emphasized the deficiency in the prediction process (accuracy = 0.24, F1 score = 0.12, and MCC = −0.24)”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Genomics scenario: colon cancer gene expression
“the best classifier resulted being k-NN (F1 score = 0.87 and accuracy = 0.81)”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Genomics scenario: colon cancer gene expression
“k-nearest neighbors | +0.48 | 0.87 | 0.80 | 0.92 | 0.52”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“only 1 healthy individual out of 9 was correctly recognized”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“TP=90, FN=1, TN=0, FP=9”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“Finally, in the last Use case C2 (positively imbalanced dataset)”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“Use case C2 Negatively imbalanced dataset | 11 | 89 | 2 | 9 | 88 | 1”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“For the Use case A1 (negatively imbalanced dataset)”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“made of 9 healthy individuals (negatives =9%) and 91 sick patients (positives =91%)”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“Also in the Use case C1 (positively imbalanced dataset)”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“90 healthy controls (negatives =90%) and 10 sick patients (positives =10%)”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“only 5 sick patients out of 70”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“TP = 5, FN = 70, TN = 19, FP = 6”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Results and discussion > Use cases
“the resulting MCC = –0.03”
§ The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation > Methods > Relationship between measures
“In red, the (−0.04, 0.95) point corresponding to use case A1”
Citations
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- Akosa JS. Predictive accuracy: a misleading performance measure for highly imbalanced data. In: Proceedings of the SAS Global Forum 2017 Conference. Cary, North Carolina: SAS Institute Inc.: 2017. p. 942–2017.
- Guilford JP. Psychometric Methods. New York City: McGraw-Hill; 1954.
- van Rijsbergen CJ, Joost C. Information Retrieval. New York City: Butterworths; 1979.
- Sasaki Y. The truth of the F-measure. Teach Tutor Mater. 2007; 1(5):1–5.
- Dubey A, Tarar S. Evaluation of approximate rank-order clustering using Matthews correlation coefficient. Int J Eng Adv Technol. 2018; 8(2):106–13.
- Suresh Babu N. Various performance measures in binary classification – An overview of ROC study. Int J Innov Sci Eng Technol. 2015; 2(9):596–605.
- Ferri C, Hernández-Orallo J, Flach PA. A coherent interpretation of AUC as a measure of aggregated classification performance. In: Proceedings of ICML 2011 – the 28th International Conference on Machine Learning. Norristown: Omnipress: 2011. p. 657–64.
Cited by
Sorted by citations
Items 301–400 of 1,016
Soft ground tunnel lithology classification using clustering-guided light gradient boosting machine
Kiliç, K., Ikeda, H., Adachi, T., & Kawamura, Y. (2023) · Journal of Rock Mechanics and Geotechnical Engineering · doi:10.1016/j.jrmge.2023.02.013 · Cited by 54
Chicco, D., & Jurman, G. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104426 · Cited by 55
Self-citationDeep learning for AI-based diagnosis of skin-related neglected tropical diseases: A pilot study
Yotsu, R. R., Ding, Z., Hamm, J., & Blanton, R. E. (2023) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0011230 · Cited by 58
Yes SIR! On the structure–inactivity relationships in drug discovery
López‐López, E., Gortari, E. F., & Medina‐Franco, J. L. (2022) · Drug Discovery Today · doi:10.1016/j.drudis.2022.05.005 · Cited by 56
Miloshevich, G., Cozian, B., Abry, P., Borgnat, P., & Bouchet, F. (2023) · Physical Review Fluids · doi:10.1103/physrevfluids.8.040501 · Cited by 53
Koç, K., Ekmekcioğlu, Ö., & Gürgün, A. P. (2023) · Journal of Construction Engineering and Management · doi:10.1061/jcemd4.coeng-12848 · Cited by 60
Phi, Fei, Fo, Fum: Effect Sizes for Categorical Data That Use the Chi-Squared Statistic
Ben‐Shachar, M. S., Patil, I., Thériault, R., Wiernik, B. M., & Lüdecke, D. (2023) · Mathematics · doi:10.3390/math11091982 · Cited by 59
“A popular metric is the Matthews correlation coefficient (MCC) for binary classifiers, which is often presented in terms of true and false positives and negatives but is nothing more than φ [14].”
BackgroundAbramovich, O., Pizem, H., Eijgen, J. V., Oren, I., Melamed, J., Stalmans, I., Blumenthal, E. Z., & Behar, J. A. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107522 · Cited by 53
“They reported an AUC of 0.947 and accuracy of 91.8% using 10-fold cross validation.”
MethodInfluentialDoes cybersecurity risk stifle corporate innovation activities?
Wang, J., Ho, C. Y., & Shan, Y. G. (2023) · International Review of Financial Analysis · doi:10.1016/j.irfa.2023.103028 · Cited by 47
Eid, A. M., Soudan, B., Nassif, A. B., & Injadat, M. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09439-x · Cited by 53
A comprehensive evaluation of oversampling techniques for enhancing text classification performance
Taskiran, S. F., Türkoğlu, B., Kaya, E., & Aşuroğlu, T. (2025) · Scientific Reports · doi:10.1038/s41598-025-05791-7 · Cited by 101
Zhu, M., Gutierrez, M. S., Zhu, H., et al. (2021) · Advanced Engineering Informatics · doi:10.1016/j.aei.2020.101232 · Cited by 52
Singh, J., Litfin, T., Singh, J., Paliwal, K. K., & Zhou, Y. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac053 · Cited by 51
“Similar trends in terms of F1, MCC, precision and AUC of ROC are observed for using all ESM-1b attention maps as shown in Supplementary Table S2.”
InfluentialMattivi, P., Pappalardo, S. E., Nikolić, N., et al. (2021) · Remote Sensing · doi:10.3390/rs13101869 · Cited by 51
“This leads to a better estimation of the classifier performance in unbalanced datasets [63].”
BackgroundMethodInfluentialPeriwal, V., Bassler, S. O., Andrejev, S., Gabrielli, N., Patil, K. R., Typas, A., & Patil, K. R. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010029 · Cited by 53
“We focused on balanced threshold metrics (Fig 1D) (Matthews correlation metric (MCC) and F1) in combination with rank metrics (Fig 1E and 1F) (precision-recall curve and ROC curves) to evaluate our model’s performance (metrics definitions explained in S3 Table).”
MethodInfluentialKarpiński, R., Krakowski, P., Jonak, J., Machrowska, A., Maciejewski, M., & Nogalski, A. (2022) · Sensors · doi:10.3390/s22103765 · Cited by 50
Rusdi, N. ’., Kasihmuddin, M. S. M., Romli, N. A., Manoharam, G., & Mansor, M. A. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101554 · Cited by 52
Anjum, & Katarya, R. (2023) · International Journal of Information Security · doi:10.1007/s10207-023-00755-2 · Cited by 57
“Whenever the given dataset is highly imbalance in that case it is found that MCC has given best results compared to the accuracy [133].”
Tian, C., Lee, Y., Song, Y., Elmasry, M. R., Yoon, M., Kim, D., & Cho, S. (2024) · ACS Applied Nano Materials · doi:10.1021/acsanm.4c00359 · Cited by 52
Privacy-Preserving Breast Cancer Classification: A Federated Transfer Learning Approach
Selvakanmani, S., Devi, G. D., Rekha, V., & Jeyalakshmi, J. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01035-8 · Cited by 56
“…uses some of the experimental analyses such as precision [21], F 1-score [21], accuracy [21], recall [21], Mathews correlation coefficient ( B MCC ) [22], specificity, [21] and classification time by using false positive values, true positive values, false negative values, and true negative values.”
Peng, D., Yazdanianasr, M., Mauricio, A., Verwimp, T., Desmet, W., & Gryllias, K. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.112266 · Cited by 54
Mushtaq, Z., & Su, S. (2020) · Symmetry · doi:10.3390/sym12111822 · Cited by 63
“Based on these factors, the implemented evaluation metrics discussed in [79,80] are described as follows: Accuracy ACC) = TP + TN TP + TN + FP + FN ∗ 100% (16) Error = 1 − ACC ∗ 100% (17) Matthews Correlation Coefficient MCC) = TP ∗ TN) − FP ∗ FN) TP + FP) TP + FN) TN + FP) TN + FN) ∗ 100% (18) Figure 10.”
MethodDecompartmentalisation as a simple color manipulation of plant-based marbling meat alternatives
Ong, S., Loo, L. S. W., Pang, M., Tan, R., Teng, Y., Lou, X., Chin, S. K., Naik, M. Y., & Yu, H. (2021) · Biomaterials · doi:10.1016/j.biomaterials.2021.121107 · Cited by 51
MalBERT: Malware Detection using Bidirectional Encoder Representations from Transformers
Rahali, A., & Akhloufi, M. A. (2021) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc52423.2021.9659287 · Cited by 67
Singh, K., & Malhotra, J. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12512-z · Cited by 50
Yan, B., Li, Y., Li, L., Yang, X., Li, T., Yang, G., & Jiang, M. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105944 · Cited by 50
Multimodal Information Fusion for Weather Systems and Clouds Identification From Satellite Images
Bai, C., Zhao, D., Zhang, M., & Zhang, J. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3202246 · Cited by 54
“The activation function is changed to Sigmoid [69] in the added fully connected layer of these networks; the loss function, further more, is replaced by binary cross entropy [73], [74].”
BackgroundDeep Learning applied to Road Accident Detection with Transfer Learning and Synthetic Images
Tamagusko, T., Correia, M. G., Huynh, M. A., & Ferreira, A. (2022) · Transportation research procedia · doi:10.1016/j.trpro.2022.09.012 · Cited by 57
Khan, A., Brouwer, N. P. M., Blank, A., Müller, F. C., Soldini, D., Noske, A., Gaus, E., Brandt, S., Nagtegaal, I. D., Dawson, H., et al. (2023) · Modern Pathology · doi:10.1016/j.modpat.2023.100118 · Cited by 49
Interpretable Machine Learning on Metabolomics Data Reveals Biomarkers for Parkinson’s Disease
Zhang, J. D., Xue, C., Kolachalama, V. B., & Donald, W. A. (2023) · ACS Central Science · doi:10.1021/acscentsci.2c01468 · Cited by 55
Feng, S., Keung, J., Xiao, Y., Zhang, P., Yu, X., & Cao, X. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121084 · Cited by 49
Shahbazian, R., & Greco, S. (2023) · IEEE Access · doi:10.1109/access.2023.3306721 · Cited by 52
Yang, Z., Wan, C., Yu, Q., et al. (2023) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2023.103331 · Cited by 50
Multi-Grained Radiology Report Generation With Sentence-Level Image-Language Contrastive Learning
Liu, A., Guo, Y., Yong, J., & Xu, F. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3372638 · Cited by 55
“To give a more comprehensive demonstration, we also show the result evaluated by the Matthews correlation coefficient (MCC) [50], which is more robust and informative on imbalanced datasets [51].”
MethodComparing Threshold Selection Methods for Network Anomaly Detection
Komadina, A., Martinić, M., Groš, S., & Mihajlović, Ž. (2024) · IEEE Access · doi:10.1109/access.2024.3452168 · Cited by 58
“For the presentation of our results, we chose to use the MCC metric because it is a more reliable statistical value and gives a high score only if the prediction performs well in all four categories of the confusion matrix: TP, FP, TN, and FN [109], [110].”
MethodRNA Sequencing-Based Identification of Ganglioside GD2-Positive Cancer Phenotype
Sorokin, M., Kholodenko, I. V., Kalinovsky, D. V., Shamanskaya, T. V., Doronin, I. I., Коновалов, Д. М., Mironov, A. A., Kuzmin, D., Nikitin, D. M., Deyev, S. M., et al. (2020) · Biomedicines · doi:10.3390/biomedicines8060142 · Cited by 52
“In order to measure statistical significance between two datasets in each comparison we used Matthew’s Correlation Coefficient (MCC) score [39].”
MethodAhmed, S., Kim, B. C., Lee, K. H., Jung, H. Y., & Initiative, A. D. N. (2020) · PLoS ONE · doi:10.1371/journal.pone.0242712 · Cited by 54
“In addition to the abovementioned metrics, we have evaluated our models with the Matthews correlation coefficient (MCC) to produce a more informative and truthful score and to avoid overly optimistic outcomes [52].”
MethodYu, J.-W., Yoon, Y.-W., Baek, W.-K., & Jung, H.-S. (2021) · Remote Sensing · doi:10.3390/rs13214282 · Cited by 53
“FAR and F1 score are defined as follows [39]:”
BackgroundEnhancing false negative and positive rates for efficient insider threat detection
AlSlaiman, M., Salman, M. I., Saleh, M. M., & Wang, B. (2023) · Computers & Security · doi:10.1016/j.cose.2022.103066 · Cited by 49
Maino, C., Misul, D. A., Mauro, A. D., & Spessa, E. (2021) · Energy and AI · doi:10.1016/j.egyai.2021.100073 · Cited by 53
Sharma, S. S., & Verma, V. K. (2021) · The Journal of Supercomputing · doi:10.1007/s11227-021-03833-1 · Cited by 50
Assessing the functional relevance of splice isoforms
del Pozo, F. C., Gómez, L. M., Walsh, T., Rodrı́guez, J. M., Domenico, T. D., Abascal, F., Vázquez, J., & Tress, M. L. (2021) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqab044 · Cited by 46
The Proposition and Evaluation of the RoEduNet-SIMARGL2021 Network Intrusion Detection Dataset
Mihăilescu, M.-E., Mihai, D., Carabaș, M., Komisarek, M., Pawlicki, M., Hołubowicz, W., & Kozik, R. (2021) · Sensors · doi:10.3390/s21134319 · Cited by 51
“In this paper, a standard set of well-known metrics was used to evaluate the approach: Accuracy (ACC), Precision (Pr), Recall (Re), F1-Score, Matthews correlation coefficient (MCC) [45], and Balanced accuracy (BCC).”
MethodSillberg, C. V., Kullavanijaya, P., & Chavalparit, O. (2021) · Journal of Ecological Engineering · doi:10.12911/22998993/141364 · Cited by 49
“The precision, recall, F1-score, and accuracy are range from 0 to 1, where the minimum to a maximum of those values depicted the poor to perfect classifi cation result [Chicco and Jurman, 2020].”
BackgroundBennett-Lenane, H., Griffin, B. T., & O’Shea, J. P. (2021) · European Journal of Pharmaceutical Sciences · doi:10.1016/j.ejps.2021.106018 · Cited by 51
Symmetrical Simulation Scheme for Anomaly Detection in Autonomous Vehicles Based on LSTM Model
Alsulami, A. A., Al-Haija, Q. S. A., Alqahtani, A., & Alsini, R. A. I. (2022) · Symmetry · doi:10.3390/sym14071450 · Cited by 57
Park, B., Shin, T.-S., Cho, J., et al. (2022) · Postharvest Biology and Technology · doi:10.1016/j.postharvbio.2022.112154 · Cited by 49
A Comprehensive Survey on Rare Event Prediction
Shyalika, C., Wickramarachchi, R., & Sheth, A. (2024) · ACM Computing Surveys · doi:10.1145/3699955 · Cited by 52
“…Matthews’s correlation coefficient (MCC) : MCC is a more accurate statistical metric that provides a reliable measure of a model’s performance by considering all four confusion matrix categories (TP, FN, TN, and FP) and balancing the impact of both positive and negative class sizes [179, 16, 180].”
FastForest: Increasing random forest processing speed while maintaining accuracy
Yates, D., & Islam, M. Z. (2020) · Information Sciences · doi:10.1016/j.ins.2020.12.067 · Cited by 53
Stacked Deep Dense Neural Network Model to Predict Alzheimer’s Dementia Using Audio Transcript Data
Khan, Y. F., Kaushik, B., Rahmani, M. K. I., et al. (2022) · IEEE Access · doi:10.1109/access.2022.3161749 · Cited by 48
Rahman, A. S. M. Z., Liu, C., Sturm, H., Hogan, A. M., Davis, R. L., Hu, P., & Cardona, S. T. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010613 · Cited by 55
“The cut-off of binarization for each model was determined by the Youden’s J statistic on the ROC curve of the validation set, which is defined as: J ¼ sensitivityþ specificity 1 PLOS Computational Biology | https://doi.org/10.1371/journal.pcbi.1010613 October 13, 2022 15 / 22 After obtaining optimum cut-offs, we compu…”
MethodInfluentialHanna, M. G., Olson, N. H., Zarella, M. D., Dash, R. C., Herrmann, M. D., Furtado, L. V., Stram, M. N., Raciti, P. M., Hassell, L. A., Mays, A., et al. (2023) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2023-0042-cp · Cited by 52
Liu, J., Liu, J., Liu, J., et al. (2021) · Marine and Petroleum Geology · doi:10.1016/j.marpetgeo.2021.104939 · Cited by 48
Prediction of Drug-Induced Liver Toxicity Using SVM and Optimal Descriptor Sets
Jaganathan, K., Tayara, H., & Chong, K. T. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22158073 · Cited by 55
“The MCC is used to measure the balanced classification performance, the coefficient value 1 indicates perfect classification and −1 represents perfect misclassification [58].”
MethodEnvisaging Employee Churn Using MCDM and Machine Learning
Chaudhary, M., Gaur, L., Jhanjhi, N. Z., et al. (2022) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2022.023417 · Cited by 48
“Matthew's Correlation Coefficient (MCC): Accuracy cannot be considered a reliable measure when the dataset is imbalanced as it provides an over-optimistic approximation of the model [45].”
BackgroundPonomarchuk, A., Burenko, I., Malkin, E., Nazarov, I., Kokh, V., Avetisian, M., & Евгеньевич, Ж. Л. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3142514 · Cited by 50
“We scored each openly accessible crowdsourced dataset by its 10 -fold averaged ROC AUC and Matthews Correlation Score (MCC), [49], independently computed using each branch of our model (sec.”
MethodMeta-Learning to Improve Unsupervised Intrusion Detection in Cyber-Physical Systems
Zoppi, T., Gharib, M., Atif, M., & Bondavalli, A. (2021) · ACM Transactions on Cyber-Physical Systems · doi:10.1145/3467470 · Cited by 49
“, if a file reports on many attacks data and a few normal data, some of the metrics above can be misleading [61], since they either (i) do not consider all the four classes of the confusion matrix, i.”
BackgroundNovel loss functions for ensemble-based medical image classification
Rajaraman, S., Zamzmi, G., & Antani, S. (2021) · PLoS ONE · doi:10.1371/journal.pone.0261307 · Cited by 52
“The authors of [31] discuss the benefits of using MCC metric over F-score and accuracy in evaluating classification models.”
MethodEvaluating Eye Movement Event Detection: A Review of the State of the Art
Startsev, M., & Zemblys, R. (2022) · Behavior Research Methods · doi:10.3758/s13428-021-01763-7 · Cited by 48
“Chicco and Jurman (2020) compared Matthews Correlation Coefficient to accuracy and F1-score in a binary classification case and concluded that MCC, contrary to the other two metrics, is robust when evaluating imbalanced data.”
BackgroundA space-embedding strategy for anomaly detection in multivariate time series
Ji, Z., Wang, Y., Yan, K., Xie, X., Xiang, Y., & Huang, J. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117892 · Cited by 47
Sobahi, N. M., Arı, B. G., Çakar, H., et al. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3151465 · Cited by 47
“The achievement of the proposed method was evaluated based on the various evaluation metrics, namely, accuracy, sensitivity, specificity, F1-Score, MCC, and kappa, respectively [31].”
MethodBanks, G. C., Ross, R. L., Toth, A., Tonidandel, S., Goloujeh, A. M., Dou, W., & Wesslen, R. S. (2022) · The Leadership Quarterly · doi:10.1016/j.leaqua.2022.101658 · Cited by 56
Metrics for Evaluating Classification Algorithms
MUNTEAN, M. I., & Militaru, F. D. (2022) · Smart innovation, systems and technologies · doi:10.1007/978-981-19-6755-9_24 · Cited by 47
Wang, H., Cao, L., Huang, C., Jia, J., Dong, Y., Fan, C., & de Albuquerque, V. H. C. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3297654 · Cited by 49
“In statistics, The F1 score [52] is derived from the precision and recall of the test, where precision is the number of true positive results divided by the total number of positive results, including those that were misidentified, and recall is the number of true positive results divided by the…”
BackgroundMalicious detection model with artificial neural network in IoT-based smart farming security
Mohy-eddine, M., Guezzaz, A., Benkirane, S., & Azrour, M. (2024) · Cluster Computing · doi:10.1007/s10586-024-04334-5 · Cited by 56
“We employed the MCC to evaluate the reliability of our binary classifier [57].”
Images and CNN applications in smart agriculture
Sakka, M. E., Mothe, J., & Ivanovici, M. (2024) · European Journal of Remote Sensing · doi:10.1080/22797254.2024.2352386 · Cited by 59
Unsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape
Zoppi, T., Ceccarelli, A., Capecchi, T., & Bondavalli, A. (2021) · ACM/IMS Transactions on Data Science · doi:10.1145/3441140 · Cited by 53
“, if a file reports on many attacks data and a few normal data, some of the metrics above can be misleading [56], since they either (i) do not consider all the four classes of the confusion matrix, i.”
BackgroundS, A. N. U. P., Susan, D., & Kumar, S. R. (2021) · Ecological Informatics · doi:10.1016/j.ecoinf.2021.101248 · Cited by 53
Magnússon, R. Í., Limpens, J., Kleijn, D., van Huissteden, J., Maximov, T. C., Lobry, S., & Heijmans, M. M. P. D. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.146877 · Cited by 45
Salem, A., Bănescu, S., & Pretschner, A. (2021) · ACM Transactions on Privacy and Security · doi:10.1145/3465361 · Cited by 47
“of the classification models because conventional metrics, such as accuracy, are unable to capture or penalize bias toward certain classes in imbalanced datasets [5].”
BackgroundToğaçar, M., Muzoğlu, N., Ergen, B., Yarman, B. S., & Halefoğlu, A. M. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103128 · Cited by 47
“When these equations are examined, abbreviations are used: sensitivity (Se), specificity (Sp), prediction (Pre), f-score (F-Scr), accuracy (Acc), true positive (TP), false positive (FP), true negative (TN), and false negative (FN) [52-56].”
Vowels, L. M., Vowels, M. J., & Mark, K. P. (2021) · The Journal of Sex Research · doi:10.1080/00224499.2021.1967846 · Cited by 63
“Arguably the best summary statistic for imbalanced classification problems is MCC (Boughorbel et al., 2017; Chicco & Jurman, 2020; Matthews, 1975).”
BackgroundThe AdaBoost Approach Tuned by Firefly Metaheuristics for Fraud Detection
Petrović, A., Bačanin, N., Zivkovic, M. Z., Marjanović, M., Antonijević, M., & Strumberger, I. (2022) · IEEE World Conference on Applied Intelligence and Computing (AIC) · doi:10.1109/aic55036.2022.9848902 · Cited by 54
“The added metrics in which the methods are compared in are the Confusion Matrix (CM), the AUC [51], and the MCC [52], [53].”
MethodMachine learning-based prediction of cognitive outcomes in de novo Parkinson’s disease
Harvey, J., Reijnders, R. A., Cavill, R., Duits, A., Köhler, S., Eijssen, L., Rutten, B. P. F., Shireby, G. L., Torkamani, A., Creese, B., et al. (2022) · npj Parkinson s Disease · doi:10.1038/s41531-022-00409-5 · Cited by 50
“This compared the PD-Dementia group ( n = 43) to a non-dementia conversion group created by combining PD-MCI and CI groups ( n = 166).”
Di, Y., You, N., Dong, J., Liao, X., Song, K., & Fu, P. (2023) · European Journal of Agronomy · doi:10.1016/j.eja.2023.126841 · Cited by 47
Convolutional neural networks to automate the screening of malaria in low-resource countries
Zhao, O. S., Kolluri, N., Anand, A., et al. (2020) · PeerJ · doi:10.7717/peerj.9674 · Cited by 46
A Study of Deep Learning-Based Face Recognition Models for Sibling Identification
Goel, R., Mehmood, I., & Ugail, H. (2021) · Sensors · doi:10.3390/s21155068 · Cited by 46
“A confusion matrix [35] summarizes prediction results on a classification algorithm or face recognition model when used to classify.”
The MCC-F1 curve: a performance evaluation technique for binary classificationPreprint
Cao, C., Chicco, D., & Hoffman, M. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.11278 · Cited by 45
“It produces a high score only if the classifier obtained optimal results in all four of the confusion matrix cells [14, 22, 23, 24].”
BackgroundSelf-citationChicco, D., & Jurman, G. (2022) · Frontiers in Robotics and AI · doi:10.3389/frobt.2022.876814 · Cited by 44
“Even if accuracy and F 1 score are very common in machine learning studies, they can be misleading (Chicco and Jurman, 2020) in several situations.”
BackgroundResultSelf-citationEmpirical charging behavior of plug-in hybrid electric vehicles
Mandev, A., Plötz, P., Sprei, F., & Tal, G. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119293 · Cited by 50
LRF : A logically randomized forest algorithm for classification and regression problems
Jain, N., & Jana, P. K. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119225 · Cited by 47
Resource optimizing federated learning for use with IoT: A systematic review
Ferreira, L., Sadok, D. F. H., & Endo, P. T. (2023) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2023.01.006 · Cited by 49
A CNN Sound Classification Mechanism Using Data Augmentation
Chu, H., Zhang, Y.-L., & Chiang, H.-C. (2023) · Sensors · doi:10.3390/s23156972 · Cited by 45
Du, R., Xin, R., Wang, H., Zhu, W., Li, R., & Liu, W. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2024.151828 · Cited by 48
Moriano, P., Hespeler, S. C., Li, M., & Mahbub, M. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11292-w · Cited by 80
“Recent studies have suggested the use of the Matthews correlation coefficient (MCC) which in general is intended to address the unbalanced data issues commonly found in anomaly detection datasets [27].”
Deep Learning for Diagnosis of Paranasal Sinusitis Using Multi-View Radiographs
Jeon, Y., Lee, K., Sunwoo, L., Choi, D., Oh, D. Y., Lee, K. J., Kim, Y., Kim, J., Cho, S. J., Baik, S. H., et al. (2021) · Diagnostics · doi:10.3390/diagnostics11020250 · Cited by 46
“In addition, Matthews correlation coefficient was calculated to measure the correlation between the prediction and ground truth label [20].”
MethodEvolving data-adaptive support vector machines for binary classification
Dudzik, W., Nalepa, J., & Kawulok, M. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107221 · Cited by 48
Hossain, S. I., de Hervé, J. D. G., Hassan, M. S., Martineau, D., Petrosyan, E., Corbin, V., Beytout, J., Lebert, I., Durand, J., Carravieri, I., et al. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106624 · Cited by 52
“As our dataset was balanced so, accuracy can be considered a good measure of predictive performance [43] and we did most of the analysis in terms of accuracy but also kept the other metrics to provide insights for experts from different domains as done in relevant studies [3,8].”
MethodAleksić, S., Seeliger, D., & Brown, J. B. (2021) · Molecular Informatics · doi:10.1002/minf.202100113 · Cited by 45
Colorimetric detection of H2O2 with Fe3O4@Chi nanozyme modified µPADs using artificial intelligence
Şen, M., Yüzer, E., Doğan, V., et al. (2022) · Microchimica Acta · doi:10.1007/s00604-022-05474-4 · Cited by 45
Wu, D., Wang, X., & Wu, S. (2021) · Entropy · doi:10.3390/e23040440 · Cited by 49
“F1 takes into account the Acc and Recall of the classification model [93], and can be regarded as the harmonic average of the accuracy and recall of the model.”
Methodroadscene2vec: A tool for extracting and embedding road scene-graphs
Malawade, A. V., Yu, S.-Y., Hsu, B., Kaeley, H., Karra, A., & Faruque, M. A. A. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108245 · Cited by 46
Arı, B. G., Siddique, K., Alçin, Ö. F., Aslan, M., Şengür, A., & Mehmood, R. M. (2022) · IEEE Access · doi:10.1109/access.2022.3181887 · Cited by 44
“The achievement of the proposed method was evaluated based on the various evaluation metrics, such as accuracy, sensitivity, specificity, precision, F1-Score, Mathew correlation (MCC), and kappa, respectively [44,45].”
MethodBaig, A. R., Abbas, Q., Almakki, R. S., Ibrahim, M. E. A., AlSuwaidan, L., & Ahmed, A. E. S. (2023) · Diagnostics · doi:10.3390/diagnostics13030385 · Cited by 44
“The MCC metric [46] is unaffected by the problem with imbalanced datasets.”
BackgroundHow to approach machine learning-based prediction of drug/compound–target interactions
Ataş, H., & Doğan, T. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00689-w · Cited by 48
“MCC incorporates all true and false predictions into the equation, and it is preferred over both accuracy and F1-score due to its robustness and reliability, especially in the cases of dataset imbalance [82].”
BackgroundMorgan, H., Madani, A. A., Hussien, H. M., & Nassar, T. (2023) · Geoscience Letters · doi:10.1186/s40562-023-00261-2 · Cited by 45
“(5, 6, 7 and 8) based on CM (Sokolova and Lapalme 2009; Chicco and Jurman 2020) in Table 2.”
Real-time ore sorting using color and texture analysis
Shatwell, D. G., Murray, V., & Barton, A. (2023) · International Journal of Mining Science and Technology · doi:10.1016/j.ijmst.2023.03.004 · Cited by 48
Explainable machine learning models for Medicare fraud detection
Hancock, J., Bauder, R. A., Wang, H., et al. (2023) · Journal Of Big Data · doi:10.1186/s40537-023-00821-5 · Cited by 44
“These metrics include f-Measure [42], geometric mean of true positive rate and true negative rate [43], Matthews Correlation Coefficient (MCC) [44], and precision [45].”
BackgroundXu, Q. A., Jayne, C., & Chang, V. I. C. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2024.123326 · Cited by 44
Pre-trained language models in medicine: A survey
Luo, X., Deng, Z., Yang, B., & Luo, M. Y. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102904 · Cited by 57
Metadata
- Authors
- Chicco, D., & Jurman, G.
- Affiliations
- Krembil Research Institute (CA)
- Fondazione Bruno Kessler (IT)
- Corresponding author
- Chicco, Davide
- Venue
- BMC Genomics
- Published
- 2020-01-02
- Volume
- 21
- Issue
- 1
- Pages
- 6-6
- ISSN
- 1471-2164 (ISSN-L)
- PMC
- PMC6941312
- OpenAlex
- W2999309192
- Semantic Scholar
- 44858b662822d291a5e1e9e3501ad19a9f1572c2
- Cited by
- 6,143
- References
- 104
- Citations by year
- 2026: 1118 · 2025: 1152 · 2024: 1060 · 2023: 1032 · 2022: 883 · 2021: 682 · 2020: 211
- FWCI
- 276.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Genomics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- BOA (University of Milano-Bicocca) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Iris (University of Trento) (submittedVersion)
- Funders
- University of Toronto
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- Matthews correlation coefficient, F1 score, accuracy, confusion matrix, class imbalance, classification metrics
- MeSH terms
- Machine Learning, Correlation of Data, Algorithms, Data Interpretation, Statistical, Computational Biology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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