What is the impact of data augmentation on machine learning models?
Data augmentation helps improve model performance by increasing the diversity of training data, reducing overfitting.
Can you give me some examples of data augmentation techniques?
Sure! Common techniques include rotation, flipping, scaling, and adding noise to images.
How does data augmentation affect the training time of a model?
It may increase training time due to the larger dataset, but the trade-off is often worth it for better accuracy.
Are there any downsides to using data augmentation?
Yes, if not done carefully, it can introduce noise or unrealistic variations that may confuse the model.
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