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Related Questions
- What are the trade-offs between undersampling and oversampling in handling imbalanced datasets?
- How does undersampling affect the model's ability to generalize to new, unseen data?
- What are the potential biases that can arise from undersampling the majority class?
- Can undersampling the majority class lead to a model that is overly confident in its predictions?
- How does the choice of undersampling method impact the performance of the model?
- What are some alternative approaches to dealing with imbalanced datasets, such as cost-sensitive learning?
- Can undersampling the majority class improve the model's performance on specific metrics, such as precision or recall?
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