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SDV

As explained in Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

SDV is an open-source software library for generating synthetic data from existing datasets using statistical and machine learning models.

SDV is an open-source library for generating synthetic tabular data from an existing dataset. It supports statistical models such as Gaussian copulas and deep-learning models based on GANs and VAEs. section 2.3.3

Expanding German Credit Risk data

A practical workflow reads 1,000 German Credit Risk records, detects and adjusts metadata, trains a TVAE synthesizer, and samples 10,000 additional rows. The generated records resemble the original distributions, but their similarity should be tested rather than assumed. section 2.3.3

How Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI covers it

How deep it goes
Explains model choices and provides an end-to-end listing, while deferring detailed neural-network internals.
Examples
Uses German Credit Risk records, metadata configuration, TVAE generation, and Random Forest-based adversarial validation.
What you can do after
Build synthetic tabular data with SDV and evaluate resemblance through adversarial validation.
How it connects
Connects synthetic data with Gaussian copulas, GANs, VAEs, metadata, and adversarial validation.
Cover of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
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This explainer was generated by Manning's knowledge graph from the text of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI. The section links point to the parts of the book it draws on.