The classic trade-off
tp = 50fp = 10fn = 5tn = 35
Result: F1 0.8696 (precision 0.8333, recall 0.9091)
F1 Score Calculator
The F1 score balances precision and recall in one number: their harmonic mean. It is the go-to single metric for imbalanced classification, where accuracy is misleading. Enter the four confusion-matrix counts and get F1, plus the precision and recall it combines.
Enter your values and press Calculate — the result appears here.
Worked examples
Perfect and terrible
tp = 30fp = 0fn = 0tn = 70
Result: F1 1.00 (perfect)
Zero positives predicted
tp = 0fp = 0fn = 15tn = 85
Result: F1 undefined — no positive predictions
Frequently asked questions
What makes F1 better than accuracy?
On imbalanced data, a model that always predicts the majority class gets high accuracy but terrible recall. F1 combines precision and recall, so it only rewards a model that is both precise and complete — no free lunch from class imbalance.
Why is the harmonic mean used instead of a simple average?
The harmonic mean is dominated by the smaller of the two values: a model with precision 1.0 but recall 0.0 gets an F1 of 0, not 0.5. It punishes lopsided performance, which is what you want from a single summary metric.
When is F1 undefined?
F1 divides by (precision + recall). If the model never predicts positive (TP + FP = 0) or there are no actual positives (TP + FN = 0), both precision and recall are undefined and so is F1.
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🔧 Related Tools
Precision Calculator
Compute precision — the share of positive predictions that were actually correct.
Try it now →Recall Calculator
Compute recall (sensitivity) — the share of actual positives the model managed to find.
Try it now →Confusion Matrix Calculator
Enter TP, FP, FN, and TN and get every derived metric: accuracy, precision, recall, specificity, F1…
Try it now →Accuracy Calculator
Calculate classification accuracy from true/false positives and negatives.
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