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Precision / Recall
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367.
High Precision Low Recall Interpretation
easy
A model has high precision but low recall. What does this mean in practice?
A
The model is aggressive; it correctly identifies most positives but generates many false alarms across the evaluated dataset
B
The model is conservative; when it predicts positive it is usually right, but it misses many true positives
C
The model is well-calibrated with balanced error rates across both the positive and negative classes
D
The model performs well overall but struggles specifically with the majority class predictions
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