Classification Metrics — Confusion Matrix & Metrics Calculator
Compute accuracy, precision, recall, F1, specificity, MCC and more for classification metrics from TP/FP/FN/TN counts.
A confusion matrix is the source of every classification metric: accuracy, precision, recall, F1, specificity and more all fall out of four counts — true/false positives and negatives. This computes them all at once and shows which one your problem should actually optimize.
Formula
Disclaimer: This tool is for general informational and estimation purposes only and is not professional financial, tax, accounting or legal advice. All figures are estimates — verify with a qualified professional before making decisions. Read the full disclaimer.
About Classification Metrics — Confusion Matrix & Metrics Calculator
A confusion matrix is the source of every classification metric: accuracy, precision, recall, F1, specificity and more all fall out of four counts — true/false positives and negatives. This computes them all at once and shows which one your problem should actually optimize. Enter the four confusion-matrix counts and this calculator returns every standard metric — accuracy, precision, recall (sensitivity), F1, specificity and the Matthews correlation coefficient — recomputed live. MCC is highlighted because it is the most honest single number for imbalanced problems: it only scores high when the model does well across all four quadrants, unlike accuracy or F1 which can be gamed.
How to use Classification Metrics — Confusion Matrix & Metrics Calculator
- 1Enter your values into Classification Metrics — Confusion Matrix & Metrics Calculator — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use Classification Metrics — Confusion Matrix & Metrics Calculator?
- ✓Computes Classification Metrics instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: precision = TP/(TP+FP).
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
Which single metric should I report?+
There isn't one — that's the point of computing them together. Use precision when false positives are costly, recall when false negatives are, F1 when you need balance, and ROC-AUC / PR-AUC when you want a threshold-independent view. Accuracy alone misleads on imbalanced data.
Why is accuracy a trap on imbalanced data?+
If 95% of cases are negative, a model that always predicts 'negative' scores 95% accuracy while catching zero positives. Precision, recall and F1 expose this immediately because they ignore the easy true-negative majority. Always check the minority-class recall.
Why is MCC considered the most reliable single metric?+
MCC uses all four confusion-matrix cells and behaves like a correlation coefficient (−1 to +1): it is high only when predictions track reality across both classes. On imbalanced data where accuracy and even F1 can mislead, MCC stays informative — which is why it's increasingly the recommended summary statistic.
What's the difference between recall and specificity?+
Recall (sensitivity) is the fraction of actual positive cases the model catches — TP/(TP+FN). Specificity is the fraction of actual negative cases it correctly clears — TN/(TN+FP). A model can have high recall and low specificity (flags everything) or vice versa; you need both to judge it.
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