Calculation Results
Review recall, related metrics, class results, and threshold behaviour.
Formula Substitution
Calculation Summary
Per-Class Recall and Supporting Metrics
| Class | TP | FN | FP | TN | Support | Recall | Precision | F1 | Specificity |
|---|
Threshold Analysis
| Threshold | TP | FN | FP | TN | Recall | Precision | F1 |
|---|
Recall by Class
Threshold Performance
Generated by the Recall Calculator for Machine Learning.
Model Comparison
Compare recall and supporting metrics across multiple binary models.
Formula Used
False Negative Rate = FN ÷ (TP + FN) = 1 − Recall
Precision = TP ÷ (TP + FP)
F1 Score = 2 × Precision × Recall ÷ (Precision + Recall)
Recall measures how many actual positives the model finds. It is also called sensitivity or true positive rate. Higher recall means fewer positive cases are missed.
Macro recall gives each class equal importance. Weighted recall uses each class support as its weight. Micro recall combines all class decisions before calculation.
How to Use
- Select the input mode matching your available data.
- Enter confusion counts, labels, CSV rows, or label sets.
- Select the positive class and averaging method.
- Choose precision, zero handling, and confidence settings.
- Submit the form and review every reported metric.
- Export the summary, tables, or printable PDF report.
Example Data
| Scenario | TP | FN | Recall | Meaning |
|---|---|---|---|---|
| Medical screening | 95 | 5 | 95% | Five positive patients were missed. |
| Fraud detection | 70 | 30 | 70% | Thirty fraudulent cases were missed. |
| Defect detection | 180 | 20 | 90% | Twenty defects were not detected. |
Frequently Asked Questions
What does recall measure?
Recall measures detected positives among all actual positives. It focuses directly on missed positive examples. The denominator includes true positives and false negatives.
Is recall the same as sensitivity?
Yes, recall and sensitivity usually mean the same metric. True positive rate is another common name. All use identical confusion matrix values.
When is high recall especially important?
High recall matters when missed positives cause serious harm. Examples include disease detection and fraud screening. The acceptable target depends on operational consequences.
Can recall be high while precision is low?
Yes, predicting many samples as positive can raise recall. That strategy may also create false positives. Precision reveals how many positive predictions were correct.
What is macro-average recall?
Macro recall averages class recall scores equally. Rare and common classes receive equal influence. It highlights poor performance on smaller classes.
What is weighted-average recall?
Weighted recall averages class scores using class support. Larger classes influence the final value more. It reflects the observed class distribution.
How does the threshold affect recall?
Lower thresholds usually predict more positive cases. Recall often increases as false negatives decrease. Precision may fall because false positives increase.
What happens when no positives exist?
Recall becomes undefined when TP plus FN equals zero. The calculator offers configurable zero handling. Empty classes may also be ignored.
Does class imbalance affect recall?
Recall remains meaningful for each individual class. Aggregate accuracy may hide minority-class failures. Per-class recall exposes those missed positives.