Oversampling

Oversampling is a technique used in data science to balance the class distribution of a dataset by increasing the number of instances in the minority class. This is done by randomly replicating instances from the minority class until the class distribution is balanced. Oversampling is commonly used in machine learning algorithms to improve the performance of models when dealing with imbalanced datasets. However, oversampling can also lead to overfitting and reduced generalization performance if not done properly. Therefore, it is important to use appropriate evaluation metrics and cross-validation techniques to ensure the effectiveness of oversampling.
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