What is data augmentation in machine learning?

What is data augmentation in machine learning?

Data augmentation is a technique used to increase the diversity of your training dataset by applying various transformations.

What are some common techniques for data augmentation?

Common techniques include rotation, flipping, scaling, cropping, and adding noise to images.

How does data augmentation help improve model performance?

It helps by providing more varied examples, which can lead to better generalization and reduced overfitting.

Can data augmentation be used for text data as well?

Yes, techniques like synonym replacement, random insertion, and back-translation can be used for text data augmentation.