85 correct out of 100
tp = 50fp = 10fn = 5tn = 35
Result: Accuracy 0.85 (85%) · Error rate 0.15
Accuracy Calculator
Accuracy is the fraction of all predictions your classifier got right: (TP + TN) ÷ (TP + FP + FN + TN). Enter the four confusion-matrix counts and see the accuracy — plus the error rate, which is 1 − accuracy.
Enter your values and press Calculate — the result appears here.
Worked examples
Perfect predictions
tp = 40fp = 0fn = 0tn = 60
Result: Accuracy 1.00 (100%)
Frequently asked questions
What does accuracy measure?
The proportion of predictions that were correct. It is the simplest classification metric, but on imbalanced data it can be misleading — 90% accuracy is weak if 90% of examples are one class and the model simply always predicts it.
Why is accuracy undefined when all counts are zero?
Accuracy divides by the total number of predictions. With no predictions at all (TP = FP = FN = TN = 0) the formula is 0 ÷ 0, which has no meaningful value.
What is the error rate?
The error rate is the share of predictions that were wrong: (FP + FN) ÷ N, which equals 1 − accuracy. Some teams report one, some the other.
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Compute the F1 score — the harmonic mean of precision and recall — from TP, FP, FN, TN.
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Compute the mean absolute error of a regression from actual vs predicted value pairs.
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