As the world moves towards Data Diplomacy, Synthetic Data Generation is becoming a must-have skill for upcoming Data-Scientists because companies, organizations are becoming vary of sharing their data cross border citing privacy issues.
Synthetic Data is programmatically generated from minimal or no original data.
Data Scientists should have in-depth knowledge of statistics and basic programming expertise to create a realistic and robust synthetic data-set.
The idea behind Synthetic Data generation is to alleviate the scarcity of data while working on real-life problems.
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Why Synthetic Data?
The cost of data acquisition is machine learning’s cold-start problem. It is prohibitive in some ways and even keeps many from entering the field. Over the past few years, though, a new data source has emerged and it’s radically changing the economics of machine learning: synthetic data. Rather than collecting and annotating data by hand, we’re getting better at creating it programmatically, and in some cases, it’s even better for training models than the stuff collected from the real world.
For starters, one might argue that freely available datasets are good-enough, but once one wants to scale-up and train complex models, the data-scarcity bug bites. And that’s where the skill to generate synthetic data becomes a necessity. Synthetic Data is not collected by any real-life survey or experiment. Its main purpose, therefore, is to be flexible and rich enough to help an ML practitioner conduct fascinating experiments with various classification, regression, and clustering algorithms.
Some of the things which need to be kept in mind while generating synthetic data are to randomize the data and the user should be able to choose a wide variety of statistical distribution to base this data upon and random noise should be interjected in a controllable manner. This synthetic data assists in teaching a system how to react to certain situations or criteria. Researcher doing clinical trials or any other research may generate synthetic data to aid in creating a baseline for future studies and testing. For example, intrusion detection software is tested using synthetic data. This data is a representation of the authentic data and may include intrusion instances that are not found in the authentic data. The synthetic data allows the software to recognize these situations and react accordingly. If synthetic data was not used, the software would only be trained to react to the situations provided by the authentic data and it may not recognize another type of intrusion.
Thus, synthetic data makes machine learning accessible to a huge number of people and aid in solving various real-life complex problems. That means we can create more data and iterate more often to produce better results. Need to add another class to your model? No problem. Need to add another keypoint to the annotation? Done.