About Balanced Accuracy Calculator
This balanced accuracy calculator averages the recall of each class, which stops a dominant class from hiding poor performance on a rare one. It is free, needs no sign-up and runs in your browser, so nothing you enter is uploaded.
Plain accuracy is the share of all predictions that are right, so a model that ignores a rare class can still score high. Balanced accuracy gives every class an equal vote. For two classes it is the mean of sensitivity and specificity.
The preloaded example has only 10 positives against 190 negatives. The model finds 5 of 10 positives and clears 180 of 190 negatives, so accuracy is 92.5% while balanced accuracy is only about 72%.
How to use Balanced Accuracy Calculator
- Choose "Matrix counts" and set the number of classes, or choose "Actual and predicted labels" and paste the two lists.
- In matrix mode, rows are the actual class and columns the predicted class. Edit the cells and the class names.
- Pick the headline metric: accuracy, precision, recall, specificity, F1, F-beta, MCC, kappa or balanced accuracy.
- For precision, recall and F-scores, choose which class counts as positive. With two classes the first class is the positive one.
- Read the headline value and its explanation, then scroll to the per-class table, the averages and the worked steps.
- Use 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.
Balanced accuracy formula
The mean of the recall of each of the K classes.
balanced accuracy = (1 / K) x sum of recall_kWorked examples
TP 5, FN 5, FP 10, TN 180 gives balanced accuracy 0.7237, accuracy 0.925
Recall of the positive class is 0.5 and specificity is 0.9474.
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.
How is balanced accuracy different from accuracy?
Accuracy weights every sample equally; balanced accuracy weights every class equally, by averaging per-class recall.
Is balanced accuracy the same as macro recall?
Yes. For a multi-class problem, balanced accuracy equals the macro average of recall.
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.
- MCC Calculator (Matthews Correlation Coefficient)Compute the MCC of a classifier from its confusion matrix, with the formula substituted.
- 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.
- 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.