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DEEPCHECKS GLOSSARY

Synthetic Data Generation

What is synthetic data and why is it important?

Synthetic data is artificial data created to protect privacy, test systems, or create training data for ML algorithms. Synthetic data production is vital because it influences the quality of simulated data; for example, synthetic data that can be reprogrammed to identify actual data is useless for privacy enhancement.

  • Synthetic data is fake data produced by various algorithms that mimic the statistical qualities of the actual data but do not expose any information about real persons.

Synthetic data is useful because it may be developed to satisfy unique demands or situations that aren’t met by existing (actual) data. This can be beneficial in a variety of situations, including:

  • When data availability or use is restricted due to privacy concerns
  • Data is required for testing a product before it is published, but such information doesn’t yet exist or is not accessible to the testers.
  • Machine learning algorithms require training data. However, such data is costly to produce in real life, specifically in the context of self-driving automobiles.

Though synthetic data was originally utilized in the 1990s, the amount of processing and memory space in the 2010s made synthetic data more widely employed.

Synthetic data in Machine Learning

Inside the ML sector, synthetic data generation is attracting interest. ML algorithms are taught using massive amounts of data, and gathering the required quantity of labeled training data might be prohibitively expensive.

Companies and researchers can use synthetically created data to establish datasets needed to pre-train ML models, a process known as transfer learning.

There are now research projects underway to advance synthetic data generation in machine learning.

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Application of Synthetic Data

Synthetic data approaches serve two industries: financial services and healthcare. The approaches can be used to generate synthetic data from real data. This allows data professionals to more freely utilize and exchange data.

For example, synthetic data allows healthcare professionals to make record-level data available to the public while maintaining patient anonymity.

Synthetic datasets, such as credit card payments that appear and behave like conventional transaction data, can aid in the detection of fraudulent behavior in the financial industry. Data scientists can utilize fake data to test or assess fraud detection systems and create new fraud detection approaches.

Synthetic data is used by DevOps teams for software testing. Artificially created data can be put into a process without removing real-world data.

How to generate Synthetic Data?

Businesses can use several approaches to conduct the data synthesis process, such as DL algorithms, decision trees, and iterative proportional fitting. They should select the approach based on the requirements to generate synthetic data for machine learning.

They should evaluate the value of synthetic data once it has been synthesized by comparing it to actual data.

The process of creating sample test data for use in running test cases is known as test data creation. There are several open source synthetic data generation tools available that generate useful data that resembles production test data.

Key takeaways

  • Working with clean data is necessary for the development of synthetic data. If you don’t clean and prepare your input before synthesis.
  • Determine if synthetic data is comparable enough to actual data for its intended application: The usefulness of synthetic varies according to the technology used to generate it. You must examine their use case and determine whether the created synthetic data is a suitable fit for the unique use case.
  • Determine your organization’s synthetic data abilities and outsource depending on the gaps in those skills. Data synthesis and preparation of data are the two most crucial phases. Suppliers can automate both stages.