👉 Recall, often denoted as \( \text{Recall}(P, S) \), is a measure of how well a model retrieves relevant items from the total possible items in a dataset. It is calculated as the ratio of correctly predicted positive instances to the total actual positive instances. Mathematically, it is expressed as:
\[
\text{Recall}(P, S) = \frac{\text{True Positives (TP)}}{\text{True Positives (TP)} + \text{False Negatives (FN)}}
\]
Where:
-
True Positives (TP)
: The number of instances correctly predicted as positive.
-
False Negatives (FN)
: The number of instances that were positive but incorrectly predicted as negative.
Recall is crucial in scenarios where missing a positive instance (false negative) is particularly costly, such as in medical diagnostics or fraud detection. A high recall indicates that the model is good at identifying most of the positive cases, even if it might also include more false positives.
Outrageously Funny Search Suggestion Engine :: Recall Math
What is the definition of Recall Math? 🙋
