What are some methods to handle imbalanced datasets in machine learning?
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One common method is undersampling the majority class to balance the dataset. Another approach is oversampling the minority class, which involves duplicating or generating new instances of the minority class. Additionally, you can use algorithms specifically designed for imbalanced datasets, such as SMOTE (Synthetic Minority Over-sampling Technique) or ADASYN (Adaptive Synthetic Sampling). Feature engineering, dimensionality reduction, and ensemble techniques can also be helpful in addressing this issue.
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