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Recall

Definition
AI-generated

Recall is the fraction of actual positives correctly identified: TP / (TP + FN).

Topics
Probability and inference
Synonyms
Sensitivity True positive rate
Related
Classification Accuracy Area Under the ROC Curve (AUC) Case-Control Study

Why it matters in GWAS

When evaluating polygenic or clinical case–control classifiers, recall at a threshold determines how many true cases are caught—relevant for screening—while specificity governs the false-positive burden.

Example usage

"We prioritized recall ≥ 0.85 for the high-risk stratum because missed cases were considered costlier than extra follow-ups."

Classification Accuracy Area Under the ROC Curve (AUC) Case-Control Study Calibration Polygenic Risk Score (PRS) Precision Specificity
Related-term network

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Node order. The current term is first. Next come every page linked from Related terms, in the same top-to-bottom order as that list. Then new terms appear in two-hop layers: for each of those direct neighbors (in that same order), their related terms are appended if not already listed. A third pass does the same for two-hop nodes. The walk stops when a fixed maximum number of nodes is reached.

References

  • Powers DM. (2011). Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J Mach Learn Technol.
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