Understanding Recall From a Confusion Matrix
Recall measures how many real positive cases your model finds. It is also called sensitivity or true positive rate. A high recall score means fewer positive cases are missed. This matters when missed positives carry risk. Fraud review, disease screening, safety alerts, and quality checks often need strong recall.
Why Recall Matters
A confusion matrix separates predictions into four counts. True positives are correct positive predictions. False negatives are real positives predicted as negative. Recall uses only those two values. It asks a direct question. Of all actual positives, how many were detected by the model?
Using the Calculator
This calculator accepts true positives, false negatives, false positives, and true negatives. The recall result appears as a decimal and as a percentage. Extra metrics provide context. Precision shows how reliable positive predictions are. Specificity shows how well negatives are rejected. Accuracy summarizes all correct predictions. F1 balances recall with precision.
Advanced Review
The tool also estimates a confidence interval for recall. This interval helps when the test sample is small. A recall of 90 percent from ten positives is less stable than the same recall from ten thousand positives. The target recall field shows the gap between current performance and your goal.
Interpreting Results
Recall should not be judged alone. A model can increase recall by predicting more cases as positive. That may create more false positives. Use recall with precision, specificity, and business cost. For critical detection systems, recall can be the main metric. For noisy workflows, balance may be better.
Practical Tips
Check that the positive class is correct before entering values. Swap labels carefully. A wrong positive class changes recall completely. Use fresh validation data when comparing models. Keep the same threshold, sample, and labeling rules across tests. Export the table when you need a clean audit record.
When recall drops, inspect false negatives first. Review common patterns, missing features, and threshold settings. Segment results by class, source, time, or user group. This can reveal hidden weakness. If recall improves after a threshold change, confirm that precision and workload remain acceptable. Good reporting connects the metric to real decisions, not only model scores. This makes future reviews faster and easier too.