ML Engineering Tools
Precision-Recall-F1 Calculator
Enter precision and recall to compute F1, F-beta score, and get macro/micro/weighted averaging guidance.
No data is transmitted — everything runs locallyTool
Example — Precision 0.85 · Recall 0.72
F1 Score
0.7796
Precision
0.85
Recall
0.72
F-beta (β=1)
0.7796
About this tool
Precision-Recall-F1 Calculator
The Precision-Recall F1 Calculator computes F1 and F-beta scores from precision and recall with macro/micro/weighted averaging guidance.
• Calculate F-beta when recall matters more than precision
• Understand macro vs micro average for multi-class eval
• Compare F1 scores between two models
• Compute weighted F1 for imbalanced classes
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FAQ
What does this tool tell you?
The Precision-Recall F1 Calculator computes F1 and F-beta scores from precision and recall with macro/micro/weighted averaging guidance.
What affects the result most?
Precision-recall tradeoff: raise threshold → higher precision, lower recall. F-beta: weights recall β× more than precision — F2 when false negatives are costly. Macro average: mean of per-class metrics, weights all classes equally.
How should I use the result?
The calculation is deterministic — the same inputs always produce the same output — so the most useful workflow is to vary one input at a time and see which factor moves the result most. That tells you where to focus your attention before committing to a decision.
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