Analyze classification outcomes using true and false prediction counts. Understand F1 performance with guided visuals. Save tables, charts, and results for classroom review tasks.
Enter confusion matrix values and choose your preferred beta weight.
This table shows sample classroom classification scenarios and their calculated performance metrics.
| Scenario | TP | FP | TN | FN | β | Precision | Recall | F1 | Fβ | Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|
| Quiz Model A | 48 | 6 | 70 | 8 | 1.00 | 88.8889% | 85.7143% | 87.2727% | 87.2727% | 89.3939% |
| Quiz Model B | 35 | 12 | 81 | 9 | 1.00 | 74.4681% | 79.5455% | 76.9231% | 76.9231% | 84.6715% |
| Essay Model C | 62 | 15 | 54 | 11 | 0.50 | 80.5195% | 84.9315% | 82.6667% | 81.3648% | 81.6901% |
| Exam Model D | 29 | 4 | 92 | 10 | 2.00 | 87.8788% | 74.3590% | 80.5556% | 76.7196% | 89.6296% |
Use β = 1 for the common F1 score. Use β greater than 1 when recall matters more. Use β below 1 when precision matters more.
This tool helps teachers, researchers, and learning analysts evaluate binary classification tasks. It can support essay screening, quiz prediction, intervention targeting, attendance alerts, or automated assessment review. The extra metrics give wider insight than F1 alone, especially when classroom datasets are imbalanced.
The F score combines precision and recall into one metric. It helps you judge how well a classifier finds positive cases while avoiding false alarms.
A confusion matrix shows correct and incorrect predictions clearly. It provides the four counts needed to calculate precision, recall, accuracy, specificity, and F based metrics.
F1 gives equal importance to precision and recall. Fβ lets you shift emphasis. Larger beta values favor recall, while smaller beta values favor precision.
Choose beta above 1 when missing positives is costly. In education, this may apply when identifying at risk students matters more than avoiding extra flags.
Yes. Accuracy can look strong when one class dominates. F score, precision, and recall reveal whether the model handles positive cases effectively.
The calculator marks that metric as undefined. This prevents misleading values when there are no predicted positives, no actual positives, or similar edge cases.
No. It also suits research methods, educational measurement, analytics lessons, and classroom data review where binary decisions need careful evaluation.
These metrics add context. MCC gives a more balanced correlation style measure, while balanced accuracy treats positive and negative classes more fairly.
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.