What are some effective data augmentation techniques for machine learning?

What are some effective data augmentation techniques for machine learning?

Data augmentation techniques include rotation, flipping, scaling, and adding noise to images. These help improve model generalization.

Can you explain how adding noise works?

Adding noise involves introducing random variations to the data, which helps the model learn to be robust against small changes.

Are there specific techniques for text data augmentation?

Yes, techniques for text include synonym replacement, random insertion, and back-translation to create diverse training samples.

How do these techniques impact model performance?

They can significantly enhance model performance by preventing overfitting and allowing the model to learn from a more diverse dataset.