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Related Questions
- Can you explain how class imbalance can affect the accuracy of the F1-score in regression problems?
- What are the limitations of using the F1-score as an evaluation metric for regression problems where the data is noisy or contains outliers?
- How does the F1-score behave when there are multiple classes with significant class overlap, and how can it be handled?
- In the case of imbalanced data, is it possible to adjust the F1-score to be more meaningful and reflective of the performance?
- Are there any strategies for choosing between different classification algorithms that report different F1-scores, such as logreg vs svm?
- What happens to the F1-score when using ensemble methods like random forest or boosting on data with significant class imbalance?
- Are there alternative metrics or methods to complement or replace the use of F1-score for regression problems where accuracy or predictive performance is not as straightforward to define?
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