Listen: Recall
Of all the actual positive cases in the data, the fraction the model correctly identified — a measure of how much the model misses.
Transcript
In machine learning, recall answers a simple question: of all the actual positive cases, how many did the model catch? To calculate it, you divide the true positives by all actual positives. A recall of eighty-five percent means the model found eighty-five percent of the genuine positives but missed fifteen percent.
You should prioritize recall when missing a positive case is costly. For instance, a search tool needs high recall to ensure it flags every query that requires a live database search. If recall is too low, the system will miss queries, leading to outdated or fabricated answers.
However, maximizing recall comes with a trade-off. If you lower the threshold to catch more true positives, you will inevitably let in more false positives, which lowers your precision. Finding the right balance depends entirely on your goals. When both errors matter equally, you can use the F1 score, which averages the two metrics.
Recall is especially critical when dealing with highly imbalanced data. A model can easily look accurate just by guessing the majority class every time, but tracking recall for the minority class will reveal whether the model is actually finding those rare, important cases.