Measure precision, recall, F1, and F beta quickly. Enter counts manually or test example data. See balanced metrics for better learning outcome analysis today.
| Scenario | TP | FP | FN | TN | Beta | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|---|
| Reading intervention screening | 42 | 8 | 6 | 44 | 1.00 | 0.8400 | 0.8750 | 0.8571 |
| Essay risk flag model | 30 | 10 | 5 | 55 | 2.00 | 0.7500 | 0.8571 | 0.8000 |
Precision = TP / (TP + FP)
Recall = TP / (TP + FN)
F1 Score = 2 × Precision × Recall / (Precision + Recall)
F-Beta Score = (1 + β²) × Precision × Recall / ((β² × Precision) + Recall)
Specificity = TN / (TN + FP)
Accuracy = (TP + TN) / (TP + FP + FN + TN)
The beta value changes the weight of recall. A higher beta gives recall more importance. A lower beta gives precision more importance.
F score helps educators judge a prediction model with balance. It combines precision and recall into one practical number. This is useful when a school tracks intervention needs, pass risk, attendance alerts, or assignment completion patterns. A single accuracy value can hide problems. F score gives a sharper view.
In education, false negatives can be costly. A missed at risk student may lose support early. False positives also matter because they can misdirect staff time. F beta scoring lets teams set the right priority. When recall matters more, choose a beta above one. When precision matters more, choose a beta below one.
A confusion matrix turns raw predictions into usable evidence. True positives show correct alerts. False positives show extra alerts. False negatives reveal missed learners. True negatives confirm stable cases. With these values, the calculator also returns precision, recall, specificity, accuracy, prevalence, support, and balanced accuracy.
Schools use these metrics for reading screening, dropout forecasting, essay classification, tutoring referral rules, and placement testing. Researchers also compare educational models with F1 and F beta because these scores are easy to explain. They work well when classes are imbalanced and one outcome appears less often.
A high F1 score often signals strong balance between precision and recall. Still, context matters. A school may accept more false positives to catch more struggling learners. Another program may need cleaner alerts. This calculator helps teams test both choices quickly and present results clearly for review, reporting, and continuous improvement.
F score is a combined metric built from precision and recall. It is useful when you want one number that reflects both correct positive predictions and missed positive cases.
F1 gives equal weight to precision and recall. F beta changes that balance. Beta above one favors recall. Beta below one favors precision.
It helps evaluate screening tools, support flags, placement systems, and learning analytics models. It is especially helpful when positive cases are rare or unevenly distributed.
Yes. This calculator includes a direct mode. Enter known precision and recall values, add beta, and the tool returns F1 and F beta immediately.
Use beta equal to one for F1. Use a higher value when missing students matters more. Use a lower value when reducing false alerts matters more.
No. Accuracy can look strong even when the model misses many important positive cases. F score gives a more balanced view for imbalanced educational datasets.
Support is the total number of actual positive cases. It equals true positives plus false negatives. It helps explain the size of the positive class.
Yes. The calculator includes a CSV export and a PDF export option. This helps with classroom reviews, audit trails, and performance reports.