Confusion Matrix Calculator
Turns a binary classifier's confusion matrix (True Positive, False Positive, False Negative, and True Negative counts) into the standard evaluation metrics: accuracy, precision (positive predictive value), recall (sensitivity/true positive rate), specificity (true negative rate), and F1 score (the harmonic mean of precision and recall).
These metrics answer different questions about a model's performance, and no single one tells the whole story. Accuracy measures overall correctness but can be misleading on imbalanced data; precision measures how trustworthy positive predictions are; recall measures how many actual positives were caught; F1 balances the latter two into one number.
Typical use cases: evaluating a machine learning classifier during coursework or research, sanity-checking a model's reported metrics by recomputing them from the raw counts, or comparing two models on the same test set.
Enter your four counts and every metric recalculates instantly, with clear labels so you don't have to memorize which formula produces which value.