About F-beta Score Calculator
This F-beta calculator generalises F1: you choose beta to say how many times more important recall is than precision, and the tool computes the score from your confusion matrix. It is free, needs no sign-up and runs in your browser, so nothing you enter is uploaded.
With beta = 1 you get F1. With beta = 2, recall counts four times as much as precision in the formula (beta squared), which suits tasks where missing a positive is costly, such as screening. With beta = 0.5 precision counts more, which suits tasks where false alarms are costly.
The preloaded example uses beta = 2 on a matrix with precision 0.75 and recall 0.6. Because recall is the lower of the two, weighting it more pulls the score down to 0.625, below F1.
How to use F-beta Score 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.
F-beta formula
Beta is how many times more important recall is than precision.
Fbeta = (1 + b^2) P R / (b^2 P + R)Worked examples
TP 60, FN 40, FP 20, TN 80, beta 2 gives F2 0.625
Precision 0.75, recall 0.6. F1 for the same matrix is 0.6667.
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 does beta mean in F-beta?
Beta sets the weight of recall relative to precision. Beta above 1 favours recall, below 1 favours precision, and 1 weights them equally.
When should I use F2 instead of F1?
When missing a positive is worse than a false alarm, such as medical screening or fraud detection where a miss is expensive.
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.
- 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.