recall (ML)

Definitions

1. The fraction of actual positive cases that the model correctly identified: true positives divided by (true positives + false negatives). High recall means the model catches most of the real positives. Recall is critical in applications like cancer screening or security threat detection where missing a positive case is dangerous.

   In plain English: Of all the things that actually were positive, how many did the model catch. High recall means very few real cases slip through the cracks.

   Example: The cancer screening model achieved 98% recall — it caught nearly every malignant case — though precision was lower, meaning some patients got unnecessary follow-up biopsies.

Related Terms

• confusion matrix
• precision (ML)
• F1 score
• K-Nearest Neighbors