AutoML generates synthetic data by applying machine learning techniques that create new data points resembling the statistical properties of real-world datasets. Common methods include Generative Adversarial Networks (GANs), variational autoencoders (VAEs), and rule-based augmentation. For example, GANs use two neural networks—a generator that creates synthetic samples and a discriminator that evaluates their realism—iteratively improving until synthetic data is indistinguishable from real data. Similarly, VAEs compress data into a latent space and reconstruct variations, enabling controlled generation. AutoML frameworks automate the selection and tuning of these techniques based on the input data type and problem context, reducing manual effort.
To ensure quality, AutoML tools validate synthetic data using metrics that compare distributions, correlations, and feature relationships between real and generated data. For tabular data, statistical tests (e.g., Kolmogorov-Smirnov for feature distributions) or similarity scores like Jensen-Shannon divergence might be used. For images, metrics like Fréchet Inception Distance (FID) assess visual fidelity. AutoML systems may also employ downstream task performance as a validation step—for instance, training a model on synthetic data and testing it on real data to check if accuracy drops. Tools like Synthetic Data Vault or AutoGluon automate these evaluations, providing developers with actionable feedback to refine generation parameters.
Practical use cases include addressing data scarcity in domains like healthcare, where generating synthetic medical images preserves patient privacy while expanding training datasets. AutoML can also balance imbalanced classes in fraud detection by creating synthetic fraud cases using techniques like SMOTE (Synthetic Minority Oversampling Technique). For example, an AutoML pipeline might analyze a dataset with 95% non-fraud transactions, automatically apply SMOTE to oversample the 5% fraud class, and validate the synthetic data’s utility via a classifier’s precision-recall scores. By automating these steps, AutoML enables developers to focus on model-building rather than manual data engineering.