Synthetic dataset generation has become an increasingly popular method in data science and machine learning (ML). This is because this technique offers controlled, scalable, and ethical ways to generate data.
You can then use this data for training, testing, and improving machine learning models, among many other impressive applications.
In this guide, we’re going to take an in-depth look at the essentials of synthetic dataset generation. We’ll examine the primary methods and tools utilized, the advantages and best practices, and the potential applications of this information.
What is a synthetic dataset?
Let’s start by looking at what we mean by synthetic datasets. Researchers can use this artificially generated dataset for research projects, clinical trials, analytics, teaching, and more.
Some of the most common applications for synthetic data include:
- Computer vision uses image datasets for object detection, facial recognition, and autonomous driving.
- Natural Language Processing (NLP): Text data generation for chatbots and translation models
- Healthcare: We simulated patient records for diagnosis prediction, treatment, and drug development.
- Finance uses transactional data for fraud detection, risk assessment, and trading.
- Robotics: Simulated environments for robotic control and navigation training
Computational methods generate synthetic data, a realistic alternative to traditional data collected from real events or interactions. Statistical, generative, or simulation-based techniques often accomplish this.
Why use synthetic data over real data?
There are several reasons that scientists and researchers might choose to use synthetic data. Generally, this is due to the unique benefits that synthetic data offers that real-world data cannot provide.
Privacy and Compliance
Synthetic data can be the ideal way to overcome privacy issues, particularly in sensitive fields like healthcare and finance. This is because strict regulations like GDPR can sometimes limit access to or use of real data.
Cost efficiency
Collecting, managing, and analyzing real data can be costly and time-consuming. However, generating synthetic data at a faster rate and a fraction of the cost can make a research project more cost-efficient and yield faster results.
Scalability
These synthetic datasets allow for generation at any scale. This provides an efficient way to train large-scale models and to scale projects up and down as required.
Data augmentation
Synthetic data can supplement existing datasets when real-world data is sparse or difficult to access, particularly in rare events or unbalanced classes.
Control and customization
Lastly, synthetic data allows researchers to control specific variables, making it possible to test model performance under a variety of different conditions. This is beneficial if these conditions are difficult to replicate in real-world situations or with real data.
Five methods of synthetic data generation
We’ve briefly discussed the various ways to generate this data. Now, let’s take a closer look at these different methods and the process behind them, taking into account the pros and cons of each.
Generative models
Generative models learn patterns in real data so that they can use them to create new, similar data points.
This is a popular method, as it can produce highly realistic synthetic data. However, it does require large amounts of training data in the first instance. Therefore, it can be considered one of the more expensive techniques.
Rule-based simulations
This method of data generation relies on statistical rules and domain-specific knowledge to create the synthetic dataset. For instance, we can use this method to generate authentic healthcare records by utilizing statistics related to age, gender, and common diagnoses.
This method is beneficial because it is highly customizable and allows for precise control over data attributes. Due to its limited variability, it may not accurately represent the real world.
Data augmentation
Data augmentation techniques manipulate real existing data to create variations that expand on that dataset.
Due to its simplicity, this technique finds widespread use. It also improves generalization in models. However, this method has limitations when it comes to creating entirely new datasets.
Agent-based modeling
This method involves creating ‘agents’ that interact within a defined environment. This method generates data that replicates real-world processes, such as modeling the spread of disease within a population in the healthcare sector.
This method can produce realistic interaction-based datasets, well-suited for complex social or economic models. That said, it is a complicated process, and it can be difficult to validate the accuracy of the results.
Procedural generation
Finally, procedural generation uses algorithms to create datasets with specific attributes. For instance, we generate synthetic traffic data by simulating road networks, cars, and traffic rules.
Again, this is beneficial because it is highly customizable and particularly suited to complex simulations. However, the development process is intense and requires detailed environment modeling to get the best results. This can make it more expensive and time-consuming.
Best practices for generating synthetic data
Now that we understand how to generate these datasets, let’s use this final section to learn about best practices for creating synthetic data:
- Define your goals and requirements early on so you understand what you want to achieve with your synthetic data.
- Ensure the synthetic data accurately reflects real-world characteristics to provide realism and fidelity.
- Avoid introducing biases as much as possible by ensuring that the data covers a wide range of scenarios, demographics, or environments. This ensures diversity and balance.
- Assess and evaluate the quality of synthetic data with quantitative metrics and qualitative reviews.
- Use a hybrid approach where you can combine synthetic and real data, as this often yields the best results. Keep in mind that you can use synthetic data to enhance and supplement real data without completely replacing it.
- Even though synthetic data typically enhances privacy, it’s crucial to confirm that it doesn’t allow the reverse engineering of real data. This helps to protect everyone involved and ensure compliance.
By choosing the method best suited to your project and keeping these best practices in mind at every stage, you can ensure you get the best results from your synthetic dataset.
