What does a high precision but low recall indicate?

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Question: What does a high precision but low recall indicate?

Options:

Correct Answer: The model is good at identifying positive cases but misses many

Solution:

High precision with low recall indicates that the model is good at identifying positive cases but misses many actual positives.

What does a high precision but low recall indicate?

What does a high precision but low recall indicate?

Step 1: Understand what precision means. Precision is the number of true positive results divided by the total number of positive results predicted by the model.
Step 2: Understand what recall means. Recall is the number of true positive results divided by the total number of actual positive cases.
Step 3: A high precision means that when the model predicts a positive case, it is usually correct.
Step 4: A low recall means that the model is missing many actual positive cases and not identifying them.
Step 5: Therefore, high precision but low recall indicates that the model is good at correctly identifying the few positive cases it does find, but it fails to find many other positive cases.

Precision – The ratio of true positive predictions to the total predicted positives, indicating how many of the predicted positive cases were actually positive.
Recall – The ratio of true positive predictions to the total actual positives, indicating how many of the actual positive cases were correctly identified.
Trade-off between Precision and Recall – A high precision but low recall scenario suggests a model that is conservative in its positive predictions, leading to fewer false positives but also missing many true positives.