About Classification Accuracy Calculator
This classification accuracy calculator divides the correct predictions by the total, and then shows the metrics that reveal whether that number can be trusted. It is free, needs no sign-up and runs in your browser, so nothing you enter is uploaded.
Accuracy is the simplest metric: the diagonal of the confusion matrix divided by everything. It is fine when the classes are balanced and the two kinds of mistake cost the same. It fails badly otherwise.
The preloaded example is the accuracy paradox. Ten people are sick and 90 are healthy, and the model finds only one sick person. Accuracy is still 91%, because it gets all 90 healthy people right. Recall of 0.1 and an MCC of about 0.3 tell the real story.
How to use Classification 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.
Accuracy formula
The number of correct predictions over the total number of predictions.
accuracy = (TP + TN) / (TP + TN + FP + FN)Worked examples
TP 1, FN 9, FP 0, TN 90 gives accuracy 0.91
A model that always predicts "healthy" would score the same 0.90 here. Recall of the sick class is 0.1 and balanced accuracy is 0.55.
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 do I calculate accuracy from a confusion matrix?
Add the diagonal cells (the correct predictions) and divide by the total of all cells. For 1, 9, 0, 90 the diagonal is 1 + 90 = 91 out of 100.
Why can high accuracy be misleading?
When one class is rare, a model that ignores it still scores high. Check recall, precision, balanced accuracy or MCC.
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.
- 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.
- 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.