Beginner · Evaluation

Recall

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. Of all the actually positive items, the fraction the model successfully found.

Technical Definition

Of all the actually positive items, the fraction the model successfully found.

How it works

Recall = TP / (TP + FN). High recall means few missed cases — vital when missing a positive is costly (cancer detection, fraud, security). The precision–recall trade-off is at the heart of every classifier tuning decision.

Related Concepts

• Precision & Recall — Complementary metrics measuring classifier accuracy on positive predictions (precision) and ability to find all positives (recall).
• Precision — Of the items the model predicted positive, the fraction that are actually positive.
• F1 Score — The harmonic mean of precision and recall, balancing both into a single number.