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Answer: All of the above
All the mentioned techniques (Early stopping, Data augmentation, Regularization, and Dropout) are effective strategies to mitigate overfitting in machine learning models by either enhancing the training data or simplifying the model's complexity.
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Which techniques can be employed to reduce overfitting in machine learning models?
A
Data Augmentation (e.g., Image scaling, rotation to enhance training data)
B
Early Stopping (a method to halt training epochs to prevent overfitting)
C
Regularization (techniques aimed at reducing model complexity)
D
Dropout (a regularization technique to prevent overfitting by randomly ignoring neurons during training)
E
All of the above