Calculator Inputs
Enter your confusion matrix counts, optional error costs, display settings, and chart mode. Results appear above this form after submission.
Example Data Table
This sample shows how precision and accuracy can differ even when the model looks strong overall.
| Example Model | TP | FP | TN | FN | Precision | Accuracy | Recall | F1 | Insight |
|---|---|---|---|---|---|---|---|---|---|
| Fraud Classifier A | 120 | 20 | 830 | 30 | 85.71% | 95.00% | 80.00% | 82.76% | High accuracy, but precision still matters for alert quality. |
| Rare Event Detector B | 30 | 45 | 900 | 25 | 40.00% | 93.00% | 54.55% | 46.15% | Accuracy looks strong, yet many positive predictions are wrong. |
Formula Used
- Accuracy =
(TP + TN) / (TP + TN + FP + FN) - Precision =
TP / (TP + FP) - Recall =
TP / (TP + FN) - Specificity =
TN / (TN + FP) - F1 Score =
2 × Precision × Recall / (Precision + Recall) - Balanced Accuracy =
(Recall + Specificity) / 2 - Negative Predictive Value =
TN / (TN + FN) - False Positive Rate =
FP / (FP + TN) - False Negative Rate =
FN / (FN + TP) - Prevalence =
(TP + FN) / Total - Predicted Positive Rate =
(TP + FP) / Total - Misclassification Rate =
(FP + FN) / Total - MCC =
(TP × TN − FP × FN) / √((TP+FP)(TP+FN)(TN+FP)(TN+FN)) - Weighted Error Cost =
(FP × FP Cost) + (FN × FN Cost) - Accuracy - Precision Gap helps reveal when overall correctness hides weak positive prediction quality.
How to Use This Calculator
- Enter the confusion matrix counts: true positives, false positives, true negatives, and false negatives.
- Add optional labels to describe the positive and negative classes clearly.
- Set false-positive and false-negative costs if your project values errors differently.
- Choose decimal places and the chart style you want for the comparison graph.
- Click Calculate Metrics to show results above the form under the page header.
- Use the CSV or PDF buttons to export the summary after calculation.
- Read the interpretation notes to decide whether precision or accuracy deserves more attention.
FAQs
1) What is the difference between precision and accuracy?
Accuracy measures all correct predictions across classes. Precision measures how many predicted positives were truly positive. When false alarms matter, precision is usually the better headline metric.
2) Why can accuracy be misleading on imbalanced data?
On imbalanced data, a model can guess the majority class and still score high accuracy. Precision reveals whether positive predictions are trustworthy when positives are rare.
3) When should I prioritize precision?
Use precision for fraud alerts, spam blocking, medical screenings, or moderation queues when false positives waste time, money, or trust.
4) Can accuracy be higher than precision?
Yes. Large true-negative counts can lift accuracy even when many positive predictions are wrong. This is common in rare-event classification.
5) What happens if there are no predicted positives?
Precision becomes undefined because TP plus FP equals zero. This calculator shows N/A and still reports accuracy, specificity, and other valid metrics.
6) Does changing the classification threshold affect both metrics?
Yes. Raising the decision threshold usually improves precision but may reduce recall. Accuracy can rise or fall depending on class balance and error mix.
7) Why does this calculator include F1 and MCC?
F1 balances precision and recall into one score. MCC uses all four confusion-matrix cells and is often more informative on imbalanced datasets.
8) Can this method work for multiclass problems?
Yes, after converting multiclass outcomes into one-vs-rest confusion matrices or by using micro, macro, or weighted averages before comparison.