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Related Questions
- How does SMOTE (Synthetic Minority Over-sampling Technique) handle imbalanced datasets, and can it be used in combination with other ensemble methods such as Random Forest or AdaBoost?
- Can SMOTE be used in conjunction with techniques like Bagging or Boosting to improve the performance of classification models?
- What are the advantages of using SMOTE with other ensemble methods, such as improved accuracy or robustness to outliers?
- How does SMOTE impact the performance of other ensemble methods, and are there any specific methods that are more suitable for use with SMOTE?
- Can SMOTE be used in combination with other oversampling techniques, such as Random Oversampling or Borderline SMOTE, to further improve performance?
- Are there any specific evaluation metrics that are more effective when using SMOTE with other ensemble methods?
- Can SMOTE be used in conjunction with feature selection methods, such as recursive feature elimination (RFE), to further improve performance?
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