What are some effective techniques for ensuring data diversity?

What are some effective techniques for ensuring data diversity?

To ensure data diversity, you can use techniques like data augmentation, stratified sampling, and synthetic data generation.

Can you explain data augmentation and how it works?

Data augmentation involves creating new training samples by applying transformations like rotation, scaling, or flipping to existing data.

What is stratified sampling?

Stratified sampling is a method where you divide your data into distinct subgroups and sample from each subgroup to maintain diversity.

How does synthetic data generation contribute to diversity?

Synthetic data generation creates artificial data points that mimic real data, helping to fill gaps and enhance diversity in datasets.

Are there any tools you recommend for these techniques?

Yes, tools like TensorFlow, Keras, and Augmentor can help with data augmentation, while libraries like SDV can assist with synthetic data generation.