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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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- 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
“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
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Items 201–300 of 1,016
Dhanraj, J. A., Kumar, R. S., Sivakumar, S., Deenadayalan, G., & Vishnuvardhan, R. (2020) · Alexandria Engineering Journal · doi:10.1016/j.aej.2020.06.041 · Cited by 71
Long, C., Jo, E., & Nam, Y. (2021) · The Journal of Supercomputing · doi:10.1007/s11227-021-04076-w · Cited by 81
Mineral Prospectivity Mapping via Gated Recurrent Unit Model
Yin, B., Zuo, R., & Xiong, Y. (2021) · Natural Resources Research · doi:10.1007/s11053-021-09979-2 · Cited by 73
Zamri, N. E., Mansor, M. A., Kasihmuddin, M. S. M., et al. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122307 · Cited by 71
A Novel Performance Measure for Machine Learning Classification
Gong, M. (2021) · International Journal of Managing Information Technology · doi:10.5121/ijmit.2021.13101 · Cited by 70
“Recent study also indicated that MCC is favourable over F1 score and accuracy in binary classification evaluation on imbalanced datasets [3].”
Fisher, C. M., Peter, K. T., Newton, S., Schaub, A. J., & Sobus, J. R. (2022) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-022-04203-3 · Cited by 75
“, those with many more observations in one class relative to the other(s)) [74, 75].”
BackgroundTomato Disease Recognition Using a Compact Convolutional Neural Network
Özbılge, E., Ulukök, M. K., Toygar, Ö., & Ozbılge, E. (2022) · IEEE Access · doi:10.1109/access.2022.3192428 · Cited by 76
Novel logic mining incorporating log linear approach
Jamaludin, S. Z. M., Romli, N. A., Kasihmuddin, M. S. M., Baharum, A., Mansor, M. A., & Marsani, M. F. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2022.08.026 · Cited by 73
Goel, L., & Nagpal, J. (2022) · IETE Technical Review · doi:10.1080/02564602.2022.2121772 · Cited by 70
“F1 score or F-measure [54] is the weighted (Harmonic) mean of Recall and Precision.”
BackgroundMethodGouveia, J. D., Galvão, T. L. P., Nassar, K. I., & Gomes, J. R. B. (2025) · npj 2D Materials and Applications · doi:10.1038/s41699-025-00529-5 · Cited by 79
Toğaçar, M., Ergen, B., & Cömert, Z. (2020) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-020-02290-x · Cited by 69
“In these equations: & FN represents the false-negative value [63, 64].”
BackgroundArı, B. G., Sobahi, N. M., Alçin, Ö. F., et al. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105311 · Cited by 69
Wang, S., Waldner, F., & Lobell, D. B. (2022) · Remote Sensing · doi:10.3390/rs14225738 · Cited by 69
“MCC measures the correlation between predictions and true labels, and is considered a more reliable metric than accuracy and F1 score [44].”
MethodAn unsupervised machine learning algorithm for in-situ defect-detection in laser powder-bed fusion
Taherkhani, K., Eischer, C., & Toyserkani, E. (2022) · Journal of Manufacturing Processes · doi:10.1016/j.jmapro.2022.06.074 · Cited by 69
Alsabhan, W. M. (2023) · Sensors · doi:10.3390/s23031386 · Cited by 69
“The F1- measure is the harmonic mean of the precision, as described by definition [40].”
MethodSurvival prediction of patients with sepsis from age, sex, and septic episode number alone
Chicco, D., & Jurman, G. (2020) · Scientific Reports · doi:10.1038/s41598-020-73558-3 · Cited by 65
Self-citationMoidu, N. P., Sharma, S., Chawla, A., Kumar, V., & Logani, A. (2021) · Clinical Oral Investigations · doi:10.1007/s00784-021-04043-y · Cited by 72
CDNet: a real-time and robust crosswalk detection network on Jetson nano based on YOLOv5
Zhang, Z.-D., Tan, M., Lan, Z.-C., Liu, H., Pei, L., & Yu, W. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07007-9 · Cited by 75
“Since the F1 score [30] can comprehensively express the precision and recall rate, we use the F1 score to evaluate the final performance.”
MethodAlboaneen, D., Alqarni, R., Alqahtani, S., Alrashidi, M., Alhuda, R., Alyahyan, E. A., & Alshammari, T. A. (2023) · Big Data and Cognitive Computing · doi:10.3390/bdcc7020074 · Cited by 69
“In addition, it is a statistical rate that is more dependable and only gives a high score if the prediction performed well in all four categories of the confusion matrix (TP, FN, TN, and FP) [74].”
BackgroundFrom time-series transcriptomics to gene regulatory networks: A review on inference methods
Marku, M., & Pancaldi, V. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011254 · Cited by 72
A review of machine learning methods for imbalanced data challenges in chemistry
Jian, J., Zhang, C., Ke, L., Hayes, N., Zhu, Y., Qiu, H., Zhang, B., Zhou, T., & Wei, G. (2025) · Chemical Science · doi:10.1039/d5sc00270b · Cited by 76
Sundaramurthy, S., & Jayavel, P. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106500 · Cited by 67
Deep Learning-Based Yoga Posture Recognition Using the Y_PN-MSSD Model for Yoga Practitioners
Upadhyay, A., Basha, N. K., & Balasundaram, A. (2023) · Healthcare · doi:10.3390/healthcare11040609 · Cited by 67
“Let {Da} be the dictionary generated for a particular yoga pose (Yi) specified by the animated image (Ia).”
BackgroundDetecting financial statement fraud using dynamic ensemble machine learning
Achakzai, M. A. K., & Peng, J. (2023) · International Review of Financial Analysis · doi:10.1016/j.irfa.2023.102827 · Cited by 82
Sentiment Analysis for TikTok Review Using VADER Sentiment and SVM Model
Isnan, M., Elwirehardja, G. N., & Pardamean, B. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.10.514 · Cited by 68
Peng, H., Wang, H., Kong, W., Li, J., & Goh, W. W. B. (2024) · Nature Communications · doi:10.1038/s41467-024-47899-w · Cited by 69
Fang, C., Fan, X., Wang, X., Nava, L., Zhong, H., Dong, X., Qi, J., & Catani, F. (2024) · Earth system science data · doi:10.5194/essd-16-4817-2024 · Cited by 67
“For accuracy evaluation, the following accuracy indicators are calculated through confusion matrices (Townsend, 1971): precision, recall, F 1 score (Chicco and Jurman, 2020), and mean intersection over union (mIoU) (Rezatofighi et al., 2019).”
MethodAutomated accurate schizophrenia detection system using Collatz pattern technique with EEG signals
Baygin, M., Yaman, O., Tuncer, T., et al. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102936 · Cited by 66
BERT, XLNet or RoBERTa: The Best Transfer Learning Model to Detect Clickbaits
Rajapaksha, P., Farahbakhsh, R., & Crespi, N. (2021) · IEEE Access · doi:10.1109/access.2021.3128742 · Cited by 75
“…categories (true positives, false negatives, true negatives, and false positives), proportionally to the size of positive and negative elements in the dataset, and therefore MCC gives better results than accuracy and F1 score [39], and as a result model comparisons using MCC value is much accurate.”
ResultMineral Prospectivity Mapping Using Deep Self-Attention Model
Yin, B., Zuo, R., & Sun, S. (2022) · Natural Resources Research · doi:10.1007/s11053-022-10142-8 · Cited by 66
“…Kappa, Matthews correlation coefficient (MCC), Precision, Recall, and F1), which can evaluate the performance of models were calculated as follows (Chicco and Jurman, 2020; Powers, 2020; Tharwat, 2020): ACC ¼ TP þ TN TP þ FP þ TN þ FN ; ð6Þ Kappa ¼ ACC Pe 1 Pe ;Pe ¼ n 2 ðTP þ FPÞ þ n2 ðFN þ TNÞ n…”
Jain, N., Tomar, A., & Jana, P. K. (2020) · Journal of Intelligent Information Systems · doi:10.1007/s10844-020-00614-9 · Cited by 68
Croft, R., Newlands, D., Chen, Z., & Babar, M. A. (2021) · Proceedings of the ... ACM-IEEE International Symposium on Empirical Software Engineering and Measurement/International Symposium on Empirical Software Engineering and Measurement · doi:10.1145/3475716.3475781 · Cited by 67
“Although the F1 Score is often used as an overall indicator of model performance, MCC provides a more reliable statistic which considers all four confusion matrix categories [5].”
MethodPandey, M. K., Arora, A., Arabameri, A., et al. (2021) · Frontiers in Earth Science · doi:10.3389/feart.2021.659296 · Cited by 65
“The F1-score does not take into account all the four primary matrices of the confusion matrix and that is where MCC (Mathew’s Correlation Coefficient) plays a decisive role as it overcomes this shortcoming by incorporating all the four matrices (Chicco and Jurman, 2020).”
BackgroundToharudin, T., Caraka, R. E., Pratiwi, I. R., Kim, Y., Gio, P. U., Sakti, A. D., Noh, M., Nugraha, F. A. L., Pontoh, R. S., Putri, T. H., et al. (2023) · IEEE Access · doi:10.1109/access.2023.3265019 · Cited by 79
“The Matthews correlation coefficient is a contingency matrix method that calculates the Pearson product-moment correlation coefficient between actual and predicted values which is formulated as follows [98], [99], [100].”
MethodMetrics for Multi-Class Classification: an OverviewPreprint
Grandini, M., Bagli, E., & Visani, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.05756 · Cited by 892
“Only in the 2000s MCC became a widely employed metric to test the performance of Machine Learning techniques with some extensions to the multi-class case [4].”
MethodDeepGene Transformer: Transformer for the gene expression-based classification of cancer subtypes
Khan, A. A., & Lee, B. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120047 · Cited by 64
Mushava, J., & Murray, M. P. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121876 · Cited by 70
Construction safety predictions with multi-head attention graph and sparse accident networks
Mostofi, F., & Toğan, V. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.105102 · Cited by 69
Rahmatinejad, Z., Dehghani, T., Hoseini, B., Rahmatinejad, F., Lotfata, A., Reihani, H., & Eslami, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-54038-4 · Cited by 65
A U-Net-Based Approach for Tool Wear Area Detection and Identification
Miao, H., Zhao, Z., Sun, C., Li, B., & Yan, R. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3033457 · Cited by 64
“Therefore, to address this second issue, a loss function based on the Matthews correlation coefficient (MCC) taking the imbalance data into account is introduced into this work.”
BackgroundInfluentialA t-SNE Based Classification Approach to Compositional Microbiome Data
Xu, X., Xie, Z., Yang, Z., Li, D., & Xu, X. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.620143 · Cited by 62
Flah, M., Ragab, M., Lazhari, M., & Nehdi, M. L. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104271 · Cited by 63
Jones, D., Rozano, L., Debler, J. W. J., Mancera, R. L., Moolhuijzen, P., & Hane, J. K. (2021) · Scientific Reports · doi:10.1038/s41598-021-99363-0 · Cited by 63
Liew, W. S., Tang, T. B., Lin, C.-H. D., & Lu, C. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106114 · Cited by 62
Effect of Hilbert-Huang transform on classification of PCG signals using machine learning
Arslan, Ö., & Karhan, M. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2021.12.019 · Cited by 62
An interpretable machine learning prognostic system for risk stratification in oropharyngeal cancer
Alabi, R. O., Almangush, A., Elmusrati, M. S., Leivo, I. V. V., & Mäkitie, A. A. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104896 · Cited by 65
Aghaabbasi, M., & Chalermpong, S. (2023) · Travel Behaviour and Society · doi:10.1016/j.tbs.2023.100640 · Cited by 65
Early cancer detection using deep learning and medical imaging: A survey
Ahmad, I., & Alqurashi, F. A. (2024) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2024.104528 · Cited by 79
Yan, K., Lv, H., Shao, J., Chen, S., & Liu, B. (2024) · Science China Information Sciences · doi:10.1007/s11432-024-4147-8 · Cited by 66
“Furthermore, binary classification metrics, including the Area Under the ROC curve (AUC) [70, 71], F 1-measure [72], and Matthews’s correlation coefficient (MCC) [73–76], are also used to investigate the prediction ability in a one-vs-rest manner.”
Automated classification of remote sensing images using multileveled MobileNetV2 and DWT techniques
Karadal, C. H., Kaya, M. C., Tuncer, T., et al. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115659 · Cited by 61
Abdullahi, S. B., & Chamnongthai, K. (2022) · Sensors · doi:10.3390/s22041406 · Cited by 65
“To overcome this daunting problem, the following indices are augmented as best choices [61]:”
BackgroundGaspar, A., Oliva, D. A., Hinojosa, S., Aranguren, I., & Zaldívar, D. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.108654 · Cited by 62
Bai, Z., Liu, Q., & Liu, Y. (2022) · Natural Resources Research · doi:10.1007/s11053-022-10100-4 · Cited by 61
“When F1 is high, the test method is effective (Chicco & Jurman, 2020).”
MethodRib fracture detection system based on deep learning
Yao, L.-D., Guan, X., Song, X., Tan, Y., Wang, C., Jin, C., Chen, M., Wang, H., & Zhang, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-03002-7 · Cited by 66
Cherian, R., & Kanaga, E. G. M. (2022) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2022.109483 · Cited by 63
Machine learning algorithm to evaluate risk factors of diabetic foot ulcers and its severity
Nanda, R., Nath, A., Patel, S., & Mohapatra, E. (2022) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-022-02617-w · Cited by 63
“It is calculated using the given formula: It can take values ranging from − 1 to + 1, the closer the value to + 1, better the trained model [30].”
MethodSSO-RBNN driven brain tumor classification with Saliency-K-means segmentation technique
Nanda, A., Barik, R. C., & Bakshi, S. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104356 · Cited by 60
Hierarchical confusion matrix for classification performance evaluation
Riehl, K., Neunteufel, M., & Hemberg, M. (2023) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1093/jrsssc/qlad057 · Cited by 61
Machine learning models predicting multidrug resistant urinary tract infections using “DsaaS”
Mancini, A., Vito, L., Marcelli, E., Piangerelli, M., Leone, R. D., Pucciarelli, S., & Merelli, E. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03566-7 · Cited by 59
PCEDNet: A Lightweight Neural Network for Fast and Interactive Edge Detection in 3D Point Clouds
Himeur, C.-E., Lejemble, T., Pellegrini, T., Paulin, M., Barthe, L., & Mellado, N. (2021) · ACM Transactions on Graphics · doi:10.1145/3481804 · Cited by 64
“For all our experiments with point clouds without normals, we obtained good classification results by estimating consistent normal vectors using Meshlab [Cignoni et al. 2008], without requiring human intervention.”
MethodStucky, B., Clark, I., Azza, Y., Karlen, W., Achermann, P., Kleim, B., & Landolt, H. (2021) · Journal of Medical Internet Research · doi:10.2196/26476 · Cited by 63
Lee, A. M., Hu, J., Xu, Y., Abraham, A. G., Xiao, R., Coresh, J., Rebholz, C. M., Chen, J., Rhee, E. P., Feldman, H. I., et al. (2022) · Journal of the American Society of Nephrology · doi:10.1681/asn.2021040538 · Cited by 65
“Models were assessed with four evaluation metrics to account for how the skewed distribution of CKD causes may overestimate model performance: receiver-operator area-under-the-curve (ROC-AUC), precision-recall area-under-the-curve (PR-AUC), F1 score, and Matthews correlation coefficient (MCC).(23,24) Significant featu…”
MethodA Comparison of Techniques for Class Imbalance in Deep Learning Classification of Breast Cancer
Walsh, R., & Tardy, M. (2022) · Diagnostics · doi:10.3390/diagnostics13010067 · Cited by 69
“Several studies [46,48] have shown MCC to be an informative metric in the study of class imbalance problems.”
BackgroundCervical Cancer Diagnostics Using Machine Learning Algorithms and Class Balancing Techniques
Glučina, M., Lorencin, A., Anđelić, N., & Lorencin, I. (2023) · Applied Sciences · doi:10.3390/app13021061 · Cited by 62
Eftekhari, A., Samadzadegan, F., & Javan, F. D. (2023) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2023.103180 · Cited by 59
Modified transfer learning frameworks to identify potato leaf diseases
Lanjewar, M. G., Morajkar, P. P., & Payaswini, P. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17610-0 · Cited by 60
Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning
Furxhi, I., & Murphy, F. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21155280 · Cited by 59
“The performance was measured with metrics such as ACC, SENS, SPEC, MCC, ROC, and precision.”
MethodInfluentialBenchmarking Bacterial Promoter Prediction Tools: Potentialities and Limitations
Cassiano, M. H. A., & Silva‐Rocha, R. (2020) · mSystems · doi:10.1128/msystems.00439-20 · Cited by 61
“It achieves high scores only if TP and TN are high, considering both types of correct classification in a single metric, and it has been shown that for this type of binary classification (e.g., promoter/nonpromoter), it is more efficient and less overoptimistic (58).”
Rifaai, T. M., Abokifa, A. A., & Sela, L. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.108271 · Cited by 58
An Artificial Intelligence Multiprocessing Scheme for the Diagnosis of Osteosarcoma MRI Images
Wu, J., Xiao, P., Huang, H., et al. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3184930 · Cited by 58
“We adopted accuracy(Acc), precision(Pre), recall(Re), F1-score(F1) [49], Intersection of Union (IoU), Dice Similarity Coefficient (DSC) [50] as the metrics evaluation to evaluate the performance of segmentation models.”
MethodKarpiński, R. (2022) · Applied Computer Science · doi:10.35784/acs-2022-14 · Cited by 58
“It is defined as the harmonic mean of model precision and recall (Chicco & Jurman, 2020).”
BackgroundMachine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts
Masoumi, M. (2023) · Journal of Marine Science and Engineering · doi:10.3390/jmse11101855 · Cited by 63
“…that both XGBoost and decision tree algorithms exhibited optimal performance, achieving average accuracy, AUROC (a graphical representation commonly used for classification model assessment [126]), and MCC (a binary classification performance metric [127]) values of 99%, 98%, and 100%, respectively.”
MethodAn Ensemble Learning Approach for Enhanced Classification of Patients With Hepatitis and Cirrhosis
Chicco, D., & Jurman, G. (2021) · IEEE Access · doi:10.1109/access.2021.3057196 · Cited by 68
“For the binary classification, we focused and ranked the results based on MCC, that is the only confusion matrix rate that generates a high score only if the classifier correctly predicts most of the data instances in proportion to the class imbalance [89], [90], rather than using other rates such as ROC AUC [91].”
MethodSelf-citationMachine Learning Methods for Quality Prediction in Production
Sankhye, S., & Hu, G. (2020) · Logistics · doi:10.3390/logistics4040035 · Cited by 57
“The F1 score depends upon which value of the class variable is being considered for the evaluation [39].”
BackgroundC-Net: A reliable convolutional neural network for biomedical image classification
Barzekar, H., & Yu, Z. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116003 · Cited by 62
“Matthews correlation coefficient (MCC), Equation 10, is another evaluation metric which produces high score when the model has achieved a superior performance on all the categories of confusion matrix including True Negative (TN), True Positive (TP), False Negative (FN), and False Positive (FP), as more unbiased and r…”
BackgroundMulti-Class Stress Detection Through Heart Rate Variability: A Deep Neural Network Based Study
Mortensen, J. A., Mollov, M. E., Chatterjee, A., Ghose, D., & Li, F. Y. (2023) · IEEE Access · doi:10.1109/access.2023.3274478 · Cited by 57
“However, MCC does not depend on which class is the positive class, and it has an advantage over the F1-score as it avoids incorrectly defining the positive class [34].”
BackgroundUnsupervised Event Detection, Clustering, and Use Case Exposition in Micro-PMU Measurements
Aligholian, A., Shahsavari, A., Stewart, E. M., Cortez, E., & Mohsenian‐Rad, H. (2021) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2021.3063088 · Cited by 63
“As explained in [45], a common evaluation criteria, such as F1score, can sometimes be misleading and show over-optimistic inflated results, especially on imbalanced data-sets; such as anomalies which inherently have low frequency compared to”
BackgroundNaseer, S., Ali, R. F., Khan, Y. D., & Dominic, P. D. D. (2021) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2021.1962738 · Cited by 58
“…score only if good results were obtained in the prediction of all four groups of the confusion matrix (true positives, false negatives, true negatives, and false positives), in proportion to both the size of positive elements and the size of negative elements in the dataset (Chicco & Jurman, 2020).”
BackgroundMethodPhinzi, K., Abriha, D., & Szabó, S. (2021) · Remote Sensing · doi:10.3390/rs13152980 · Cited by 55
“The F1-score was also reported as the harmonic mean of UA and PA [61].”
BackgroundPerez, A. G., Abd-Elrahman, A. H., Wilkinson, B., Johnson, D. J., & Carthy, R. R. (2022) · Remote Sensing · doi:10.3390/rs14163937 · Cited by 58
cardioToxCSM : A Web Server for Predicting Cardiotoxicity of Small Molecules
Iftkhar, S., de Sá, A. G. C., Velloso, J. P. L., Al‐Jarf, R., Pires, D. E. V., & Ascher, D. B. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00822 · Cited by 56
Canbek, G., Temizel, T. T., & Sağıroğlu, Ş. (2022) · SN Computer Science · doi:10.1007/s42979-022-01409-1 · Cited by 60
“Chicco and Jurman suggested MCC as a more informative metric compared with F1 and ACC [20].”
Pawan, P., & Dhiman, R. (2022) · Measurement Sensors · doi:10.1016/j.measen.2022.100616 · Cited by 56
Yuan, X., Chen, S., Zhou, H., Sun, C., & Yuwen, L. (2022) · Information Sciences · doi:10.1016/j.ins.2022.12.056 · Cited by 54
Omotehinwa, T. O., Oyewola, D. O., & Dada, E. G. (2023) · Healthcare Analytics · doi:10.1016/j.health.2023.100218 · Cited by 56
Optimizing Intrusion Detection Systems in Three Phases on the CSE-CIC-IDS-2018 Dataset
Songma, S., Sathuphan, T., & Pamutha, T. (2023) · Computers · doi:10.3390/computers12120245 · Cited by 66
Calf circumference value for sarcopenia screening among older adults with stroke
Inoue, T., Maeda, K., Shimizu, A., Nagano, A., Ueshima, J., Sato, K., & Murotani, K. (2020) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2020.104290 · Cited by 57
Kim, M.-K., Yang, H., & Kim, J.-H. (2020) · Remote Sensing · doi:10.3390/rs12213654 · Cited by 60
“In statistical analyses, the F1 score represents the harmonic average between precision and sensitivity, as follows [48]:”
BackgroundEvaluation of VGG-16 and VGG-19 Deep Learning Architecture for Classifying Dementia People
Bagaskara, A., & Suryanegara, M. (2021) · International Conference of Computer and Informatics Engineering (IC2IE) · doi:10.1109/ic2ie53219.2021.9649132 · Cited by 61
MalBERTv2: Code Aware BERT-Based Model for Malware Identification
Rahali, A., & Akhloufi, M. A. (2023) · Big Data and Cognitive Computing · doi:10.3390/bdcc7020060 · Cited by 56
Zheng, S., Wang, C., Zio, E., & Liu, J. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109872 · Cited by 58
McCay, K. D., Hu, P., Shum, H. P. H., Woo, W. L., Marcroft, C., Embleton, N. D., Munteanu, A., & Ho, E. S. L. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3138185 · Cited by 55
“In addition to this, we also calculate the Matthews Correlation Coefficient (MCC) [32], which is regarded as a reliable measure where there are significant differences between the classes sizes, and as such it is widely used as a consistent performance metric on imbalanced datasets [7], [12].”
MethodBentoumi, M., Daoud, M., Benaouali, M., & Ahmed, A. T. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12058-0 · Cited by 60
“For the multiclass case, we take the average of all the calculated likelihood values as follows: f) Matthews correlation coefficient ( MCC ) [5, 51]: this metric represents the correlation between the observed and predicted classifications, and it is computed as follows:”
BackgroundMethodPavel, A. M., O’Toole, J. M., Proietti, J., Livingstone, V., Mitra, S., Marnane, W. P., Finder, M., Dempsey, E. M., Murray, D. M., & Boylan, G. B. (2022) · Epilepsia · doi:10.1111/epi.17468 · Cited by 54
BenchMetrics: a systematic benchmarking method for binary classification performance metrics
Canbek, G., Temizel, T. T., & Sağıroğlu, Ş. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06103-6 · Cited by 51
Effect of deep transfer and multi-task learning on sperm abnormality detection
Abbasi, A., Miahi, E., & Mirroshandel, S. A. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104121 · Cited by 55
Baskozos, G., Themistocleous, A. C., Hébert, H. L., Pascal, M. M. V., John, J., Callaghan, B. C., Laycock, H. C., Granovsky, Y., Crombez, G., Yarnitsky, D., et al. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01890-x · Cited by 57
“Results: Random Forest (MCC = 0.28, AUPRC = 0.76) and Adaptive Regression Splines (MCC = 0.29, AUPRC = 0.77) were the best performing models and showed the smallest reduction in performance between the training and validation dataset.”
InfluentialJeon, Y.-J., Hasan, M. M., Park, H. W., Lee, K. W., & Manavalan, B. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac243 · Cited by 53
“Considering that we deal with a balanced training datasets and imbalanced independent datasets, MCC is an appropriate metric that has been recommended in previous studies [47, 48].”
Hepatitis C Virus Detection Model by Using Random Forest, Logistic-Regression and ABC Algorithm
Li, T. S., Chiu, H.-J., & Kuo, P. (2022) · IEEE Access · doi:10.1109/access.2022.3202295 · Cited by 54
“In recent years, it has become an extensively utilized measurement standard in the testing of machine-learning performance [55], and it is suitable for use in targeting different classes under a multiclass condition [56].”
Shi, X., Gu, L., Jiang, T., Zheng, X., Dong, W., & Tao, Z. (2022) · Remote Sensing · doi:10.3390/rs14194924 · 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).
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