What is synthetic data?
As machine learning frameworks such as Tensorflow and PyTorch become easier to use and pre-designed models for computer vision and natural language processing become more common and powerful, a significant challenge data scientists face is data collection and processing.
Businesses often struggle to collect large amounts of data within a specific time frame to train accurate models. Manually labeling data is expensive and time-consuming to retrieve data. Synthetic data is an innovation that can help data scientists and businesses overcome these barriers and develop reliable machine-learning models faster.
Synthetic data sets are not constructed from records of actual events but are created by a computer program. The primary purpose of synthetic datasets is to provide a generic and robust way to train machine learning models.
Synthetic data useful for machine learning classifiers must have certain properties. Data can be categorical, binary, or numeric, but the dataset must be randomly generated. The random process to generate the data should be controllable and based on specific statistical distributions. You can also place random noise in your data set.