Recall

Recall is a metric used in binary classification problems to answer the following question: What proportion of actual positives was predicted correctly?

Recall = 1 means the model’s predictions are perfect, all truly positive samples was predicted as the positive class.

Example:
A medical test with high Recall will identify a large proportion of the true disease cases. However, the same test might be over-predicting the positive class and give many false positive predictions!

Definition

Recall is defined as:

\[\text{Recall} = \frac{\text{True positive}}{\text{True positive} + \text{False negative}}\]

Where
True positive is when actual positive is predicted positive, and
False negative is when actual positive is predicted negative.

Read more about this in the Confusion matrix entry in the glossary.

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