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Related Questions
- What are the advantages of using F1-score over accuracy in imbalanced datasets?
- In what situations is the F1-score more informative than precision or recall alone?
- Can you provide examples of real-world applications where the F1-score is a more suitable evaluation metric?
- How does the F1-score compare to other metrics like ROC-AUC or log loss in terms of its ability to capture class imbalance?
- What are some common pitfalls to avoid when using the F1-score, and how can they be mitigated?
- In what scenarios is the F1-score more sensitive to the choice of threshold compared to other metrics?
- Can you discuss the trade-offs between using the F1-score versus other metrics like mean squared error or mean absolute error in regression tasks?
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