Generate Synthetic Data Quickly and Efficiently with Synthcity

Full Article: 2301.07573
Citation: @misc{https://doi.org/10.48550/arxiv.2301.07573,doi = {10.48550/ARXIV.2301.07573},url = {https://arxiv.org/abs/2301.07573}, author = {Qian, Zhaozhi and Cebere, Bogdan-Constantin and van der Schaar, Mihaela},keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},title = {Synthcity: facilitating innovative use cases of synthetic data in different data modalities},year = {2023},
copyright = {Creative Commons Attribution 4.0 International}}

Introduction to Synthetic Data and Synthcity

Data is king — but not all data is easily accessible or suitable for model development. Issues like data scarcity, privacy concerns, and bias often limit progress, particularly in high-stakes domains such as healthcare, finance, and education. Synthetic data promises to bridge this gap by offering high-fidelity, privacy-preserving, and unbiased datasets that can fuel innovation.

Enter Synthcity, an open-source library designed to simplify and accelerate synthetic data generation. Synthcity empowers practitioners to create synthetic datasets across a diverse range of tabular data modalities — static…