What is the purpose of synthetic data in machine learning?

The purpose of synthetic data in machine learning primarily revolves around the ability to train models without relying on real-world data examples. Synthetic data is artificially generated rather than obtained from real-world events, and this can provide numerous benefits such as mitigating privacy concerns, addressing data scarcity, and enabling the creation of diverse training scenarios that might not be present in existing datasets.

Using synthetic data allows researchers and practitioners in the field to focus on specific characteristics or edge cases of data which may not be represented in limited real datasets. For instance, if real-world data is imbalanced or contains sensitive information, synthetic data can be tailored to overcome these issues, thus providing a more robust training ground for machine learning models. This is crucial in applications where data acquisition is challenging or ethical constraints limit the availability of real data.

Other options touch on aspects of data handling or model understanding, but they do not capture the primary utility of synthetic data in the context of facilitating a broader and more ethical approach to model training in machine learning projects.