A diagnostic evaluation tool

Confusion Matrix Calculator

Also known as an error matrix. Enter the four cells of a 2×2 confusion matrix to compute precision, recall, specificity, NPV, MCC, F-beta, and the full family of derived diagnostic statistics.

Enter the matrix

Predicted condition

Positive

Negative

Actual +

True Positive

False Negative

Actual −

False Positive

True Negative

F-β weight

Quick examples

Derived statistics

Total samples: —

Positive class metric Negative class metric

Accuracy ACC

—

(TP + TN) / N

Matthews CC MCC

—

(TP·TN − FP·FN) /
√((TP+FP)(TP+FN)(TN+FP)(TN+FN))

F-β score F1

—

(1+β²)·P·R / (β²·P + R)

Predictive values

+Precision PPV

—

TP / (TP + FP)

−Neg. Pred. Value NPV

—

TN / (TN + FN)

+False Disc. Rate FDR

—

FP / (FP + TP), = 1 − PPV

−False Omission FOR

—

FN / (FN + TN), = 1 − NPV

True and false rates

+Sensitivity TPR, Recall

—

TP / (TP + FN)

−Specificity TNR

—

TN / (TN + FP)

−False Pos. Rate FPR

—

FP / (TN + FP), = 1 − TNR

+False Neg. Rate FNR

—

FN / (TP + FN), = 1 − TPR

Distribution and error

+Positive prevalence

—

(TP + FN) / N

−Negative prevalence

—

(TN + FP) / N

Balanced Accuracy

—

(TPR + TNR) / 2

Error rate

—

(FP + FN) / N, = 1 − ACC

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