Synthetic tabular data has become increasingly popular in recent years, as it offers a cost-effective answer to data augmentation for machine learning model improvement and sensitive data privacy. However, it is important to ensure that the synthetic data generated is of high quality and accurately represents the real data. To achieve this, it is essential to use reliable evaluation metrics that can assess the similarity and the distance between the synthetic and real data. In this article, we will discuss several widely used evaluation metrics for synthetic tabular data. We will explore how these metrics work, and how they can be applied to evaluate the quality of synthetic data for different types of data features. Understanding these evaluation metrics is crucial for anyone working with synthetic tabular data, as it ensures that the generated data is reliable and effective for training machine learning models.
My original article is published in Quant Blog of Quantmetry.