Skip to content
ToolUlkaa Tool
All tools

Machine Learning

TensorFlow PlaygroundConfusion Matrix CalculatorROC Curve and AUC CalculatorK-Means Clustering VisualizerGradient Descent VisualizerDecision Tree BuilderClassifier PlaygroundPCA VisualizerActivation Function Plotter

Categories

AcademicTextMathematicsStatisticsMachine LearningAIData ScienceFile
All tools
Machine Learning

MCC Calculator (Matthews Correlation Coefficient)

Compute the MCC of a classifier from its confusion matrix, with the formula substituted.

Input

Rows are the actual class, columns are the predicted class. With two classes the first one is the positive class.

Actual \ Predicted
positive
negative
2 favours recall, 0.5 favours precision.

Matthews correlation (MCC)

0.7509

MCC above 0.7 means a strong relationship between predictions and truth, and it stays honest on imbalanced data.

Accuracy
0.875
Balanced accuracy
0.875
MCC
0.7509
Cohen's kappa
0.75
Macro F1
0.8749
Total
200

Per-class metrics

ClassTPFPFNTNSupportPrecisionRecallSpecificityF1F-beta (1)
positive851015901000.89470.850.90.87180.8718
negative901510851000.85710.90.850.8780.878

Working

  1. 1Matthews correlation
    TP⋅TN−FP⋅FN(TP+FP)(TP+FN)(TN+FP)(TN+FN)\frac{TP\cdot TN - FP\cdot FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}}(TP+FP)(TP+FN)(TN+FP)(TN+FN)​TP⋅TN−FP⋅FN​
    ↓
    85⋅90−10⋅15(95)(100)(100)(105)=0.7509\frac{85\cdot 90 - 10\cdot 15}{\sqrt{(95)(100)(100)(105)}} = 0.7509(95)(100)(100)(105)​85⋅90−10⋅15​=0.7509

    MCC runs from -1 (always wrong) through 0 (no better than chance) to +1 (perfect).

Formulas

Accuracy

accuracy=TP+TNTP+TN+FP+FN\text{accuracy} = \frac{TP+TN}{TP+TN+FP+FN}accuracy=TP+TN+FP+FNTP+TN​

Precision

precision=TPTP+FP\text{precision} = \frac{TP}{TP+FP}precision=TP+FPTP​

Recall (sensitivity)

recall=TPTP+FN\text{recall} = \frac{TP}{TP+FN}recall=TP+FNTP​

Specificity

specificity=TNTN+FP\text{specificity} = \frac{TN}{TN+FP}specificity=TN+FPTN​

F1 score

F1=2PRP+RF_1 = \frac{2PR}{P+R}F1​=P+R2PR​

F-beta score

Fβ=(1+β2) PRβ2P+RF_\beta = \frac{(1+\beta^2)\,PR}{\beta^2 P + R}Fβ​=β2P+R(1+β2)PR​

MCC

MCC=TP⋅TN−FP⋅FN(TP+FP)(TP+FN)(TN+FP)(TN+FN)\mathrm{MCC} = \frac{TP\cdot TN - FP\cdot FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}}MCC=(TP+FP)(TP+FN)(TN+FP)(TN+FN)​TP⋅TN−FP⋅FN​

Cohen's kappa

κ=po−pe1−pe\kappa = \frac{p_o - p_e}{1-p_e}κ=1−pe​po​−pe​​

Balanced accuracy

1K∑k=1Krecallk\frac{1}{K}\sum_{k=1}^{K}\text{recall}_kK1​∑k=1K​recallk​
  1. Tools
  2. Machine Learning
  3. Confusion Matrix Calculator
  4. MCC Calculator (Matthews Correlation Coefficient)

About MCC Calculator (Matthews Correlation Coefficient)

This MCC calculator returns the Matthews correlation coefficient for a binary or multi-class confusion matrix, with the arithmetic written out. It is free, needs no sign-up and runs in your browser, so nothing you enter is uploaded.

MCC is the correlation between the predicted and the true labels. It uses all four cells of the confusion matrix, so it is not fooled by class imbalance the way accuracy is. It runs from -1 (every prediction wrong) through 0 (no better than guessing) to +1 (perfect).

A model scores a high MCC only if it does well on both classes, which is why many researchers prefer it to F1 for imbalanced data. If any of the four row or column totals is zero, the denominator is zero and MCC is undefined, which this tool reports as n/a.

How to use MCC Calculator (Matthews Correlation Coefficient)

  1. 1Choose "Matrix counts" and set the number of classes, or choose "Actual and predicted labels" and paste the two lists.
  2. 2In matrix mode, rows are the actual class and columns the predicted class. Edit the cells and the class names.
  3. 3Pick the headline metric: accuracy, precision, recall, specificity, F1, F-beta, MCC, kappa or balanced accuracy.
  4. 4For precision, recall and F-scores, choose which class counts as positive. With two classes the first class is the positive one.
  5. 5Read the headline value and its explanation, then scroll to the per-class table, the averages and the worked steps.
  6. 6Use the copy button to take the metrics into your notes or report.

Features

  • Binary and multi-class matrices up to 8 classes, or up to 12 classes built from pasted labels.
  • Accuracy, precision, recall, specificity, F1, F-beta, MCC, Cohen's kappa and balanced accuracy in one place.
  • Per-class true positive, false positive, false negative and true negative counts.
  • Macro, micro and weighted averages for multi-class problems, so you can see how each differs.
  • Worked steps with your numbers substituted into every formula, and the formulas rendered in LaTeX.
  • A ratio with a zero denominator is shown as n/a instead of a misleading 0.
  • Plain-English reading of the headline metric, including a warning about accuracy on imbalanced data.
  • Editable class names, so the output reads like your own problem.

MCC formula

For two classes MCC is the covariance of predictions and truth divided by the product of their spreads.

MCC = (TP TN - FP FN) / sqrt((TP+FP)(TP+FN)(TN+FP)(TN+FN))

Worked examples

  • TP 85, FN 15, FP 10, TN 90 → gives MCC 0.7509

    Numerator 85 x 90 - 10 x 15 = 7500. Denominator is the square root of 95 x 100 x 100 x 105. Accuracy is 0.875.

Privacy: how it works

  • The matrix and labels you enter are processed by code running in your browser tab. They are not sent to Ulkaa or anyone else.

Tips

  • Do not rely on accuracy alone when one class is much rarer than the others. Check recall for that class and the MCC.
  • Precision and recall trade against each other. Moving a classifier's decision threshold raises one and lowers the other; the ROC and AUC calculator shows the whole trade-off.
  • F1 gives equal weight to precision and recall. Use F-beta with beta above 1 when missing a positive is worse than a false alarm, and below 1 when the reverse is true.
  • A metric with an empty denominator, such as precision for a class never predicted, is shown as n/a and left out of macro averages.

Frequently asked questions

Is it really free?

Yes. There is no price, no trial and no credit card. Ulkaa Tool does the work with your own device, so there is no server bill to pass on to you.

Do I need an account?

No. There is no sign-up and no login. Open the page and use it. An Ulkaa account is not needed for anything on this tool.

Does it run in my browser?

Yes. Your matrix and labels are processed by code running in your browser tab, on your own device.

Is my data uploaded?

No. Your matrix and labels are never sent to Ulkaa or to anyone else. You can check this yourself by watching the Network tab in your browser developer tools while you use the tool.

What is a good MCC value?

Above 0.7 is strong, 0.3 to 0.7 is moderate and near 0 means no better than chance. A negative MCC means predictions tend to be the opposite of the truth.

Is MCC better than F1?

For imbalanced data it is often more reliable, because it counts true negatives and treats both classes symmetrically. F1 ignores true negatives and depends on which class you call positive.

More Confusion Matrix Calculator pages

  • Confusion Matrix CalculatorEnter a confusion matrix, or paste actual and predicted labels, and get every classification metric with the working.
  • Precision and Recall CalculatorEnter your confusion matrix to get precision and recall with the formula worked through.
  • F1 Score CalculatorGet the F1 score of a classifier from its confusion matrix, with every step shown.
  • Classification Accuracy CalculatorWork out accuracy from a confusion matrix and see when it misleads.
  • Cohen's Kappa CalculatorMeasure agreement beyond chance between two raters or between a model and the truth.
  • Sensitivity and Specificity CalculatorEnter a test's counts to get sensitivity (recall) and specificity, with the working shown.
  • F-beta Score CalculatorChoose beta to weight recall or precision more, and get the F-beta score with its working.
  • Balanced Accuracy CalculatorAverage the recall of every class so a rare class counts as much as a common one.
  • Multi-class Confusion Matrix CalculatorScore a classifier with three or more classes, class by class and on average.
  • Confusion Matrix From Actual and Predicted LabelsPaste the true labels and the predictions and get the matrix and its metrics.

Ulkaa Apps

  • Ulkaa
  • Calendar
  • Task
  • Note
  • Disk
  • Call
  • Contact
  • Mail
  • Finance
  • Health
  • Office
  • Work
  • Workspace
  • Halum
  • Jaal
  • Islam
  • Game
  • Weather
  • Campus
  • News
  • Developer
  • Help
  • Account
About·Privacy·Terms·© 2026 Ulkaa

What is Ulkaa Tool?

Ulkaa Tool is a free collection of fast, privacy-friendly online tools from Ulkaa Network. Calculate your GPA and CGPA, edit rich text, count words and characters, replace text and more — all in your browser.

Every tool runs entirely in your browser — no sign-up, and your data never leaves your device.

Academic

  • GPA Calculator
  • CGPA Calculator
  • Citation Generator
  • Quiz Maker

Text

  • Rich Text Editor
  • Word Counter
  • Character Counter
  • Text Replacement

Ulkaa

  • Ulkaa
  • Ulkaa News
  • Ulkaa Campus
  • Ulkaa Tool
  • Ulkaa Game
  • Ulkaa Weather
  • Ulkaa Islam

Company

  • About Ulkaa
  • Careers
  • Support
  • Help
  • Privacy Policy
  • Terms of Service
  • Disclaimer
  • Sitemap
© 2015-26, Ulkaa. All rights reserved.
Facebook PageLinkedIn CompanyX (Twitter) ProfileInstagram ProfileGitHub Account