Precision Calculator

Step-by-Step Guide

1. 1Enter True Positive Count: Type the number of correct positive predictions your model made, based on your confusion matrix or evaluation results.
2. 2Enter False Positive Count: Type the number of incorrect positive predictions — cases your model flagged as positive that were actually negative.
3. 3Choose an Output Format: Select Decimal, Percentage, or Both depending on how you need to report the result.
4. 4Adjust Decimal Precision: Choose how many decimal places to display, from 0 to 5, to match the precision your reporting requires.
5. 5Review the Live Result: The precision score, percentage, performance rating, and full calculation steps update instantly as you type.
6. 6Copy, Export, or Share: Copy the full report, download it as CSV, TXT, or JSON, print it, or copy a shareable URL with your inputs encoded.

Key Features

• ✓Instant precision calculation with a 150ms debounce as you type
• ✓Decimal, percentage, or both output formats
• ✓Automatic performance rating from Needs Improvement to Excellent
• ✓Full formula breakdown with substituted values
• ✓Adjustable decimal precision from 0 to 5 places
• ✓Quick example presets for spam filtering, fraud detection, and medical screening
• ✓Clear division-by-zero handling with a plain-language explanation
• ✓Shareable calculation URL using query parameters
• ✓Export report as CSV, TXT, or JSON, plus a printable layout
• ✓Calculation history — save and reload up to 20 past results
• ✓All processing runs locally — no data leaves your browser

Pro Tips

• 💡Always report precision alongside recall — a model can hit 100% precision by making only one very safe positive prediction, which tells you nothing about its overall usefulness.
• 💡Use the F1 score when you need a single number balancing both precision and recall, especially when your classes are imbalanced.
• 💡Set your decision threshold based on the real-world cost of a false positive versus a false negative for your specific use case, not a default 0.5 cutoff.
• 💡Track precision over time as you retrain your model — a sudden drop often signals data drift or a labeling issue in your training set.
• 💡When comparing two models, make sure you're evaluating both on the same test set and threshold, since precision is sensitive to both.

Common Mistakes to Avoid

• ✕Don't confuse precision with accuracy — accuracy considers all four confusion matrix cells (TP, TN, FP, FN), while precision only looks at predicted positives.
• ✕Don't optimize for precision alone on an imbalanced dataset — a model that rarely predicts positive can achieve high precision while missing most real positive cases (low recall).
• ✕Don't forget that precision says nothing about False Negatives — a model can have excellent precision and still miss the majority of true positive cases.
• ✕Don't compare precision scores across models evaluated on different test sets or different class distributions — the numbers aren't directly comparable.
• ✕Don't treat a single precision number as the full picture — always pair it with recall, and consider the real business cost of each type of error.