Big Data Analytics and Visualization
Oversampling is a technique used in data preprocessing where the number of instances in the minority class of a dataset is increased to balance the class distribution. This approach helps improve the performance of machine learning models, particularly when working with imbalanced datasets where one class significantly outnumbers the other. By artificially generating more instances of the minority class, oversampling seeks to prevent models from being biased towards the majority class and ensures better generalization and accuracy.
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