What are data augmentation techniques in machine learning?

What are data augmentation techniques in machine learning?

Data augmentation techniques are methods used to increase the diversity of your training dataset without actually collecting new data. This can help improve the performance of machine learning models.

Can you give me some examples of these techniques?

Sure! Common techniques include image rotation, flipping, scaling, cropping, and adding noise. For text, you might use synonym replacement or back-translation.

How do these techniques help in improving model performance?

They help by providing more varied data, which can reduce overfitting and make the model more robust to different inputs.

Are there any downsides to using data augmentation?

Yes, if not done carefully, it can introduce noise or irrelevant variations that may confuse the model instead of helping it.