Confusion Matrix Calculator

Enter the four cells of a binary confusion matrix and instantly compute accuracy, precision, recall, F1, specificity, MCC, and more. Useful for evaluating classifiers and A/B test outcomes.

Definitions

Accuracy = (TP + TN) / (TP + TN + FP + FN) — overall correctness.

Precision = TP / (TP + FP) — of predicted positives, how many are actually positive.

Recall / Sensitivity / TPR = TP / (TP + FN) — of actual positives, how many were caught.

Specificity / TNR = TN / (TN + FP) — of actual negatives, how many were correctly rejected.

F1 Score = 2 · (Precision · Recall) / (Precision + Recall) — harmonic mean of precision and recall.

MCC (Matthews correlation coefficient) — balanced measure that works well even with class imbalance, ranges from −1 to +1.

Balanced Accuracy = (TPR + TNR) / 2 — useful when classes are imbalanced.

Prevalence = (TP + FN) / total — fraction of actual positives in the data.