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Supervised Learning
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Data Issues
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Imbalanced data handling
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632.
Purpose of Oversampling
easy
What does oversampling the minority class aim to achieve in imbalanced learning?
A
It reweights the loss function so the model is penalized more heavily for majority class errors
B
It generates additional labeled data by augmenting the training set with real-world minority class examples collected after the original dataset
C
It reduces the total training set size by matching the minority class count to the majority class count
D
It increases the representation of the minority class in training to reduce the model's bias toward the majority
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