what is synthetic data

Synthetic data is information that is artificially generated rather than produced by real-world events. It is created algorithmically and can be used as a substitute for real-world data sets to validate mathematical models and to train machine learning models. Synthetic data is generated to meet specific needs or certain requirements, and it can be deployed to validate mathematical models and to train machine learning models. Synthetic data is used in a variety of fields as a filter for information that would otherwise compromise the confidentiality of particular aspects of the data. Synthetic data is important because it can provide several benefits over real-world data, such as being less expensive, easier to generate, and customized to specific needs. The largest application of synthetic data is in the training of neural networks and machine learning models, as the developers of these models need carefully labeled data to train their models. Synthetic data is also used to protect sensitive data and mitigate bias.