In the context of evaluation metrics, what does recall measure?

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Question: In the context of evaluation metrics, what does recall measure?

Options:

Correct Answer: The ability of a model to identify all relevant instances

Solution:

Recall, also known as sensitivity, measures the proportion of actual positives that are correctly identified.

In the context of evaluation metrics, what does recall measure?

In the context of evaluation metrics, what does recall measure?

Step 1: Understand that recall is a metric used in evaluating the performance of a model, especially in classification tasks.
Step 2: Recall focuses on the positive class, which means it looks at the instances that are actually positive.
Step 3: Recall measures how many of the actual positive instances were correctly identified by the model.
Step 4: To calculate recall, use the formula: Recall = True Positives / (True Positives + False Negatives).
Step 5: True Positives are the cases where the model correctly predicted a positive outcome, while False Negatives are the cases where the model incorrectly predicted a negative outcome instead of positive.

Recall – Recall measures the proportion of actual positive cases that are correctly identified by a model.
Sensitivity – Sensitivity is another term for recall, emphasizing its role in identifying true positive cases.