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Related Questions
- What are some common metrics used to evaluate how well a model handles out-of-distribution data?
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- What metrics can be used to assess the robustness of a model to data augmentation or perturbations?
- Can you explain the concept of calibration and how it relates to model performance with negative examples?
- What are some common methods for evaluating the sensitivity of a model to specific types of noise or corruption?
- How do you evaluate the performance of a model on a dataset with a large proportion of negative examples?
- What metrics are used to assess the model's ability to generalize to unseen data, including negative examples?
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