What is Synthetic Data in Machine Learning?

In machine learning, artificially created data is referred to as “synthetic data,” as opposed to data gathered from actual sources. It mimics the statistical characteristics of authentic data, aiding in model training and testing when real data is limited or sensitive. Techniques such as data augmentation or generative models create synthetic data, enhancing model robustness and performance. Despite its usefulness, ensuring synthetic data accurately represents real-world scenarios is crucial for effective model generalization.

How is synthetic data generated?

Synthetic data is created using algorithms and statistical models that analyze real-world data to identify its underlying patterns and distributions. These patterns are then used to generate new data points that resemble the real data but do not contain any of the original information.

The figure represents the structure of the synthetic data that retains the same structure as the original data but is different from each other.

It is generated in various techniques and methods each technique modifies specific data characteristics and based on application requirements.

• Random sampling technique to generate synthetic data:
  Data generation through random sampling involves the creation of data points by randomly selecting values according to statistical distributions observed in real-world data. This method, while straightforward, has limitations in accurately representing the intricate interdependencies found in authentic datasets. Real-world data often exhibits specific statistical patterns, such as Gaussian distributions or skewed structures, which may not be fully captured by the simplicity of random sampling.
• Bootstrapping technique to generate synthetic data:
  The application of the Bootstrapping technique involves resampling from an existing dataset with replacement, thereby creating synthetic datasets that preserve statistical properties observed in the original data. This method works by randomly drawing samples from the original dataset, allowing individual data points to be selected more than once. Through this process, Bootstrapping generates multiple synthetic datasets, each mimicking the statistical characteristics of the original data. This technique is particularly valuable for estimating population parameters, constructing confidence intervals, and assessing the variability of statistical measures by iteratively resampling from the observed dataset, thus providing a robust and versatile tool in statistical analysis.
• Rule based with domain specific to generate synthetic data:
  Synthetic data is created based on predefined rules and constraints, applying domain-specific knowledge of relationships and dependencies within the data. For example, in finance, rules may dictate the correlation between income and expenditure. The system systematically applies these rules, considering constraints and interdependencies, to generate realistic and representative synthetic datasets. By incorporating domain expertise, Rule-Based Systems enable the controlled synthesis of data that aligns with the intricacies of real-world scenarios, offering a tailored and interpretable approach to synthetic data generation in contexts where explicit rules govern the underlying structure and relationships within the data.
• Generating Synthetic data using statistical method:
  Statistical model techniques for synthetic data generation involves employing parametric models, such as Gaussian distributions, to replicate the statistical properties of real-world data. The process begins by identifying the underlying distribution and parameters that characterize the observed data. For instance, if data follows a Gaussian distribution, the mean and standard deviation are estimated. The statistical model then generates synthetic data points by sampling from the identified distribution, ensuring that the generated data aligns with the statistical characteristics of the original dataset. This approach is effective for capturing complex relationships and dependencies present in real-world data, providing a controlled and interpretable method for synthetic data generation based on the statistical patterns observed in the target domain.
• Building Generative Adversarial Network(GAN) to generate synthetic data:
  Generative Adversarial Networks (GANs) are instrumental in obtaining synthetic image data by leveraging a two-part system: a generator and a discriminator. The generator creates synthetic images, while the discriminator evaluates their authenticity. Initially, the generator produces images from random noise. Simultaneously, the discriminator learns to differentiates between original and synthetic images. This adversarial process continues iteratively, with both components improving their capabilities. The generator refines its output to become more convincing, and the discriminator enhances its discernment. Ultimately, GANs reach equilibrium, resulting in the generation of realistic synthetic image data that closely resembles the patterns and features of real-world images. The power of GANs lies in their ability to capture complex structures and variations, making them a key technology for applications in image synthesis, enhancement, and manipulation.

Measures of synthetic data

Measuring the accuracy of synthetic data is important to ensure its effectiveness in machine learning applications. Here are some the methods which measures the accuracy of synthetic data which are listed below,

1. Chi-square test is a measure to find the differences between the observed and expected frequencies of values in synthetic and real datasets. Lower chi-square values indicate higher accuracy and vice versa.
2. Kernel Density Estimation which compares the probability density functions of synthetic and real data using kernel density estimation. Closer match in shapes indicates greater accuracy.
3. Mean Squared Error which computes the average squared differences between individual data points in synthetic and real datasets. Lower MSE values indicate greater similarity.
4. Wasserstein Distance which measures the distance between probability distributions by calculating the minimum “cost” of turning one distribution into the other. Lower Wasserstein distance signifies higher accuracy.
5. Kolmogorov-Smirnov statistic to compare the cumulative distribution functions (CDFs) of the synthetic and real data. A smaller statistic suggests better similarity.

In data science, synthetic data is referred to as artificially generated data that replicates the statistical characteristics and patterns of real-world data. It serves various purposes in data analysis, machine learning, and deep learning. It enables machine learning researchers and data scientists to conduct experiments, test algorithms, and develop models without exposing sensitive or private information. Using algorithms and mathematical models, synthetic data is created to simulate the complexities found in real datasets. It can also be used in existing datasets, especially in cases where the existing data is limited or biased. Furthermore, it facilitates the assessment of model robustness, generalization, and performance under various scenarios.