Synthetic Data for Machine Learning

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Diversity Issues in Synthetic Data

This chapter introduces you to a well-known issue in the field of synthetic data, which is generating diverse synthetic datasets. It discusses different approaches to ensure high diversity in large-scale datasets. Then, it highlights some issues and challenges in achieving diversity for synthetic data.

In this chapter, we’re going to cover the following main topics:

• The need for diverse data in ML
• Generating diverse synthetic datasets
• Diversity issues in the synthetic data realm

The need for diverse data in ML

As we have discussed and seen in previous chapters, diverse training data improves the generalizability of ML models to new domains and contexts. In fact, diversity helps your ML-based solution to ...