Calculate accuracy percentage from correct answers and questions attempted on a test, a measurement’s percent accuracy, or classification accuracy.

Questions you answered. To score against every question, enter the total here.

Total questions in the exam

Related Calculators

Accuracy Formula

For observed binary classifications, enter all four confusion-matrix counts. N = TP + TN + FP + FN, and standard accuracy = (TP + TN)/N × 100%. The calculator also reports balanced accuracy, precision, recall, specificity, F1, error rate and the majority-class baseline.

Balanced accuracy = [TP / (TP + FN) + TN / (TN + FP)] / 2 × 100%

Balanced accuracy averages the two class-specific correct-classification rates. Both actual classes must be represented. Precision = TP/(TP + FP), recall = TP/(TP + FN), specificity = TN/(TN + FP), and F1 = 2TP/(2TP + FP + FN). A metric with a zero denominator is shown as Not defined. F1 is zero when TP is zero but FP or FN is nonzero.

Confusion-Matrix Terms

  • TP: positive cases predicted positive; correct.
  • TN: negative cases predicted negative; correct.
  • FP: negative cases predicted positive; incorrect.
  • FN: positive cases predicted negative; incorrect.

Enter nonnegative whole counts with a positive total. For TP = 80, TN = 70, FP = 20 and FN = 30, standard accuracy is 150/200 = 75%. Balanced accuracy is [80/110 + 70/90]/2 = 75.252525%.

Interpreting Classification Results

There is no universal acceptable accuracy percentage. Compare the result with the majority-class baseline, the consequences of false positives and false negatives, and performance on a representative evaluation sample. A classifier that always predicts negative can achieve 95% accuracy in a sample with 95% negatives while detecting no positive cases.

The displayed nominal 95% Wilson interval uses z = 1.96 for the standard accuracy proportion. It assumes independent cases and an appropriate binomial sampling model; it does not quantify balanced-accuracy uncertainty or correct for an unrepresentative sample. See the NIST proportion-interval guidance and classification metric definitions.

Measurement Error Mode

Enter observed and nonzero reference values in the same unit. Absolute percent error = |observed − reference|/|reference| × 100%. The displayed relative-error complement is 100% minus that error. For observed 98 and reference 100, percent error is 2% and the complement is 98%. For observed 400 and reference 100, the complement is −200%.

This conventional complement can be negative and is not a probability or a metrological definition of accuracy. One observation does not establish measurement uncertainty, repeatability or systematic bias. NIST measurement terminology explains the distinction.

Prevalence Planning Mode

Expected accuracy = sensitivity × prevalence + specificity × (1 − prevalence), using proportions. With sensitivity 90%, specificity 95%, prevalence 10% and 1,000 planned cases, expected accuracy is 94.5%, with TP = 90, TN = 855, FP = 45 and FN = 10. Expected positive predictive value is 66.666667% and expected negative predictive value is 98.843931%.

Expected counts can be fractional; actual confusion-matrix counts cannot. Planning assumes the entered sensitivity and specificity transfer to the proposed population. Predictive values change with prevalence, and zero predicted-class denominators remain undefined. These estimates do not include sampling uncertainty.