Accuracy Calculator
Calculate accuracy, error rate, correct predictions, incorrect predictions, and total observations from confusion matrix values. Accuracy Calculator helps analysts, researchers, and machine learning professionals evaluate classification model performance.
Highlights
- Calculates classification accuracy from confusion matrix values.
- Uses true positives, true negatives, false positives, and false negatives.
- Displays accuracy and error rate as percentages.
- Shows correct and incorrect prediction counts.
- Calculates the total number of predictions evaluated.
Table of Contents
How Accuracy Calculator Works?
The Accuracy Calculator evaluates a binary classification system using four confusion-matrix counts: true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN). A true positive is a positive case identified correctly, while a true negative is a negative case identified correctly. False positives and false negatives are incorrect classifications.
The total number of observations is the sum of all four outcomes:
Correct predictions are the cases classified accurately, and incorrect predictions are the cases classified inaccurately:
Accuracy is the percentage of all observations classified correctly. The error rate is the percentage classified incorrectly:
For example, using TP = 80, TN = 90, FP = 10, and FN = 20 produces 170 correct predictions, 30 incorrect predictions, and 200 total predictions.
| Output | Calculation | Result |
|---|---|---|
| Accuracy | (80 + 90) / 200 × 100 | 85% |
| Error Rate | (10 + 20) / 200 × 100 | 15% |
| Correct Predictions | 80 + 90 | 170 |
| Incorrect Predictions | 10 + 20 | 30 |
| Total Predictions | 80 + 90 + 10 + 20 | 200 |
Practical Use Cases
Machine learning professionals can enter validation or test-set confusion-matrix counts to summarize how often a binary classification model produces correct predictions and to compare model versions.
Data analysts can use the calculator to report accuracy, error rate, and total observations when preparing classification-performance dashboards or evaluation reports.
Medical researchers can assess a binary diagnostic system by entering correctly detected cases, correctly rejected cases, missed cases, and incorrect positive results. Accuracy should be considered alongside clinically relevant measures such as sensitivity and specificity.
Quality-control engineers can measure the performance of automated inspection systems that classify products as acceptable or defective, including both missed defects and false alarms.
Researchers and students can verify confusion-matrix exercises by checking that correct and incorrect predictions add up to the total number of observations and that accuracy and error rate sum to 100% when the total is nonzero.
Accuracy and Limitations
The calculator performs exact arithmetic from nonnegative integer counts ranging from 0 to 1,000,000,000 and rounds or displays the resulting percentages according to the calculator's output formatting. Accuracy is undefined when TP + TN + FP + FN equals zero, because there are no observations in the denominator. The result is designed for a binary confusion matrix and does not distinguish the different consequences of false positives and false negatives; it also does not calculate precision, recall, specificity, F1 score, class-balanced accuracy, confidence intervals, or performance across multiple classes. A high accuracy can therefore be misleading when one class is much more common than the other.
Quick Examples
Example #1: Balanced Model Performance
Evaluate a classification model with strong performance across both positive and negative predictions.
Example #2: High-Accuracy Diagnostic Test
Measure the accuracy of a diagnostic system that produces very few incorrect classifications.
Example #3: Imbalanced Classification Results
Assess model accuracy when negative cases greatly outnumber positive cases.
