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At some point, you might just lack data points to learn the distribution properly. The more columns you add, the more combinations appear. Theoretically, it is a valid approach, but it would not scale if we increase the dataset’s complexity. To do so, we need to learn an approximated distribution or process compatible with the real data (i.e., a generative model) that can later be used to sample structurally and statistically comparable synthetic data. We also need to maintain the structure of the real data. Having similar statistical properties means that we need to reproduce the distribution to the extent that we should ultimately be able to infer the same conclusion from both versions of the data - synthetic and real. The end goal with synthetic tabular data generation is to take real data source and create a synthetic data source with similar statistical properties out of it.
#Explain syn how to#
How to generate synthetic data The logic behind synthetic data generation
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Although, the techniques we mentioned have been studied and used for unstructured data generation as well. In this post, we’ll focus on our field of expertise, the generation of synthetic tabular data. Therefore, teams can use synthetic data more freely for analytics, training machine learning models, testing, research and more.Īs previously explained in Types of synthetic data and real-life examples, there are different synthetic data types: structured and unstructured. This newly generated data does not contain sensitive information (PII).It breaks down data into groups and handles each one with the model best suited to its characteristics.
#Explain syn software#
The Statice software follows a hybrid approach to synthetic data generation.They each have pros and cons, and the choice of model depends on your data's type and nature.Deep learning models such as generative adversarial networks (GAN) and variational autoencoders (VAE) are well suited for synthetic data generation.The more complex the real dataset, the more difficult it is to map dependencies correctly.Generating synthetic data comes down to learning the joint probability distribution in an original, real dataset to generate a new dataset with the same distribution.We present two models to generate tabular synthetic data and explain which approach we decided to follow at Statice. We present the logic behind synthetic tabular data generation and the role of deep learning in the process. In this article, we talk about how to generate synthetic data. Synthetic data is a collective term, and not all synthetic data has the same characteristics. Synthetic datasets are not simply a re-design of a previously existing data but is a set of completely new data points. As opposed to real data, which is derived from people's information, synthetic data generation is based on machine learning algorithms.