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Related Questions
- What is the difference between calibration and accuracy in the context of machine learning models?
- How does calibration impact the reliability of a model's predictions on unseen data?
- Can you provide examples of real-world applications where calibration is crucial for handling out-of-distribution inputs?
- What are some common methods for calibrating machine learning models, and how do they differ from each other?
- How can overconfident predictions lead to poor performance in real-world settings, and what are the consequences of this?
- What is the relationship between calibration and explainability in AI models, and how do they interact?
- Can you discuss the trade-offs between calibration and other important considerations, such as accuracy and interpretability?
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