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Related Questions
- How does precision impact the model's output when the cost of false positives is high?
- What is the effect of using recall-oriented metrics on the model's prioritization of precision?
- How does the choice of evaluation metric influence the model's decision-making process in classifying imbalanced datasets?
- Can you explain how precision-recall trade-off is affected by the model's confidence threshold?
- What are the implications of using a metric that prioritizes precision over recall in a real-world application?
- How does the choice of evaluation metric impact the model's performance on a dataset with varying class distributions?
- What are some common evaluation metrics that can help balance precision and recall in a machine learning model?
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