Recall
Of all actual positives, what proportion did the model identify. High recall means few false negatives - the model finds most of the positive cases.
In-Depth Explanation
Recall (sensitivity, true positive rate) measures how well the model finds all positive cases. It answers: "Of everything that was actually positive, how much did we catch?"
Recall formula: Recall = True Positives / (True Positives + False Negatives)
Recall focus:
- Minimises false negatives
- Important when missing positives is costly
- Trade-off with precision typically
High recall needed when:
- Missing positive cases is dangerous/costly
- Comprehensive coverage is required
- False negatives have severe consequences
Examples:
- Disease screening (missing cancer is catastrophic)
- Security threats (missing attacks is dangerous)
- Defect detection (missing defects causes recalls)
Business Context
Optimise for recall when missing positives is dangerous. Better to investigate false alarms than miss real threats.
How Clever Ops Uses This
We configure AI systems for appropriate recall levels for businesses, especially in safety-critical or high-stakes applications.
Example Use Case
"A manufacturing defect detector with 95% recall catches 95 out of 100 defective products - only 5 slip through."