Running summarizing tools on a new article

Abstract: In this chapter, we will explore how to generate synthetic data for regression, classification, and clustering problems using Python. First, we will discuss how to generate synthetic data from a known distribution. Next, we will apply Gaussian noise to a regression model. Then, we will discuss how to generate synthetic data for classification and clustering problems using the Friedman functions and symbolic regression. Finally, we will generate synthetic data for tabular data using GANs.