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Related Questions
- What are the primary goals of oversampling, undersampling, and SMOTE in active learning?
- How do oversampling and undersampling differ in terms of handling class imbalance?
- What are the potential drawbacks of oversampling, such as overfitting and loss of generalizability?
- Can you explain the concept of SMOTE and its algorithmic approach to oversampling the minority class?
- How does SMOTE balance the need for representative samples with the risk of overfitting?
- What are the trade-offs between oversampling, undersampling, and SMOTE in terms of computational resources and model performance?
- Can you provide examples of scenarios where each of these techniques might be preferred over the others?
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