Track true predictions using simple confusion matrix fields. See accuracy percentage, error rate, and totals. Download CSV or PDF summaries for reporting and study.
Layout adapts to large, tablet, and mobile screens using a 3 / 2 / 1 column grid.
| Model | TP | TN | FP | FN | Accuracy |
|---|---|---|---|---|---|
| Spam Filter A | 120 | 310 | 25 | 18 | 90.91% |
| Fraud Flag B | 88 | 412 | 34 | 26 | 89.29% |
| Classifier C | 205 | 190 | 41 | 29 | 84.95% |
Use the same column structure for audits, comparisons, or classroom exercises.
Accuracy measures correct predictions across all observations.
Accuracy = (TP + TN) / (TP + TN + FP + FN)
Error Rate is the share of incorrect predictions.
Error Rate = 1 - Accuracy
Precision checks predicted positives that were truly positive.
Precision = TP / (TP + FP)
Recall checks actual positives correctly found by the model.
Recall = TP / (TP + FN)
Specificity checks actual negatives correctly rejected.
Specificity = TN / (TN + FP)
F1 Score balances precision and recall.
F1 = 2 × (Precision × Recall) / (Precision + Recall)
Model accuracy is a baseline performance indicator that compares correct predictions with total predictions. Teams use it to monitor whether a system remains dependable after deployment. Stable accuracy often reflects consistent features, labels, and threshold settings. Still, accuracy should be checked with class balance. A model may look strong overall while missing rare but important cases. This calculator helps users verify that risk by adding supporting metrics beside the main score.
The confusion matrix supplies the four values required for evaluation. True positives count correct positive predictions, and true negatives count correct negative predictions. False positives and false negatives capture the two mistake directions. These values show not only how often a model is right, but how it fails. This is useful in fraud checks, screening workflows, diagnosis support, and moderation systems, where each error type can carry a different operational cost.
Professional reviews rarely stop at accuracy alone. Precision, recall, specificity, and F1 score reveal behavior that accuracy can hide. Precision measures the quality of predicted positives. Recall measures how many actual positives were captured. Specificity evaluates negative class rejection. F1 score balances precision and recall when results are uneven. Balanced accuracy is also included because it gives equal weight to both classes and improves interpretation for imbalanced datasets.
Evaluation results become more valuable when documented consistently. This calculator supports structured reporting through CSV and PDF exports, making model checks easier to archive and compare. Analysts can use exported records in review meetings, QA summaries, and audit evidence. The example table also demonstrates a repeatable benchmarking format. Consistent documentation improves transparency, supports reproducibility, and reduces confusion when several teams assess the same model over time. This practice also helps managers trace metric changes clearly back to data revisions, model retraining dates, and threshold updates during planned release cycles safely.
Use confusion matrix values from one dataset split, such as validation or test data. Avoid mixing runs that use different thresholds, labels, or sampling rules. Confirm totals against source reports before exporting. If metrics change sharply, inspect class balance, drift, and labeling quality. Clean inputs produce trustworthy calculations, stronger comparisons, and better decisions for tuning, approval, and ongoing performance monitoring.
1. What does this calculator measure?
It calculates model accuracy from TP, TN, FP, and FN values. It also reports precision, recall, specificity, F1 score, balanced accuracy, prevalence, and total samples.
2. Can I use decimal values for confusion matrix inputs?
Yes. The form accepts numeric inputs with decimals. This can help when values come from weighted datasets, averaged folds, or normalized evaluation summaries.
3. Why is balanced accuracy included?
Balanced accuracy reduces bias from class imbalance. It averages recall and specificity, making it more informative than raw accuracy when one class is much larger.
4. When should I prioritize recall over accuracy?
Prioritize recall when missing positives is costly, such as fraud, medical screening, or safety alerts. Accuracy can look strong while recall remains too low.
5. What is included in the CSV and PDF exports?
Exports include model and dataset labels, confusion matrix counts, total samples, and all calculated metrics, making records ready for audits, reviews, and documentation.
6. How do I ensure reliable results?
Use values from one dataset split, verify label quality, keep thresholds consistent, and recheck totals. Reliable inputs are essential for meaningful model comparisons.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.