Synthetic data generation

 Synthetic data generation is the process of using generative algorithms to create artificial, AI-generated datapoints that are statistically and structurally identical to their real-world counterparts. These generative models use data samples as training data and learn the correlations, statistical properties, and data structures of the samples.

There are several different approaches for creating synthetic data. These range from basic techniques that simply draw numbers to more sophisticated methods that rely on statistical machine learning models.

Generative modeling is one of the most advanced techniques for generating synthetic data. These models are able to automatically discover the underlying model in the data and use that model to produce new datapoints that closely match the distribution of the real-world data they were trained on. This approach is useful for a variety of reasons, including allowing analysts to use the data without having to know exactly what the underlying model is.

It can also be used to generate imputation data, which is a class of techniques that replace missing values with realistic ones that are more reflective of the original value. Imputation data is often used for microsimulations to predict how different scenarios will affect outcomes.

For example, synthetic data can be used to generate imputation data for a simulation of traffic flow or public transportation usage. This technique can help planners and policy makers understand how different situations could impact these aspects of their environment.

Another common use of synthetic data is for testing software. In the financial services industry, for instance, software testers often need to test their applications with realistic inputs covering edge cases and unusual combinations of inputs to ensure that their application will perform as intended.

In addition, these tests can be used to test the scalability of the application to handle larger volumes of data. This is particularly important for applications that deal with large amounts of cash, such as automated trading systems.

Besides being used for software testing, synthetic data can also be used to train AIML models. This is a process called transfer learning, and it can dramatically accelerate the convergence of the real data model.

These models can be highly accurate, which is critical for ensuring that the final outcome of an AIML initiative will be meaningful and relevant. However, it is critical to be sure that the data produced by these generative models are high-quality and not overfitting to the original data.

The first step in assessing the quality of your synthetic data is to assess the level of missing data. If the amount of missing data is too large, then it will be difficult for the generative model to accurately learn the statistical structure of your data. In such a case, it may be necessary to remove some columns or rows from your data.

There are a few ways to do this, such as analyzing the correlation matrix or by looking at the number of synthetic records you have generated. If the number of synthetic records is significantly lower than 5,000, then it is likely that your synthetic data is not performing as well as you would like.