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Related Questions
- How does a low F1-score indicate that a model is not generalizing well to new data?
- What are the implications of a low F1-score on the model's ability to adapt to changing data distributions?
- Can a low F1-score be improved by increasing the size and diversity of the training dataset?
- How does the F1-score relate to the model's ability to handle out-of-distribution data?
- What are some strategies to address a low F1-score and improve the model's generalizability?
- How does a low F1-score impact the model's performance on tasks with concept drift?
- Can a low F1-score be a result of overfitting or underfitting, and how can it be addressed?
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