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The data used during training in any given application space is directly tiedto the performance of the system once deployed. While there are many otherfactors that go into producing high performance models within machine learning,there is no doubt that the data used to train a system provides the foundationfrom which to build. One of the underlying rule of thumb heuristics used withinthe machine learning space is that more data leads to better models, but thereis no easy answer for the question, "How much data is needed?" This workexamines a modulation classification problem in the Radio Frequency domainspace, attempting to answer the question of how much training data is requiredto achieve a desired level of performance, but the procedure readily applies toclassification problems across modalities. The ultimate goal is determining anapproach that requires the least amount of data collection to better inform amore thorough collection effort to achieve the desired performance metric.While this approach will require an initial dataset that is germane to theproblem space to act as a \textit{target} dataset on which metrics areextracted, the goal is to allow for the initial data to be orders of magnitudesmaller than what is required for delivering a system that achieves the desiredperformance. An additional benefit of the techniques presented here is that thequality of different datasets can be numerically evaluated and tied togetherwith the quantity of data, and ultimately, the performance of the architecturein the problem domain.

Training from Zero: Radio Frequency Machine Learning Data Quantity Forecasting