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Related Questions
- How does the model's recall metric impact its performance on unseen data?
- Can a model with high recall on in-distribution data still perform poorly on out-of-distribution data?
- What are some strategies to improve a model's ability to handle out-of-distribution data?
- How does the concept of 'calibration' relate to a model's ability to handle out-of-distribution data?
- Can a model's recall be improved by using techniques such as data augmentation or domain adaptation?
- What are some common pitfalls when evaluating a model's performance on out-of-distribution data?
- How does the model's ability to handle out-of-distribution data impact its interpretability and trustworthiness?
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