Synthetic Training Dataset
A Synthetic Training Dataset is a training dataset that is artificially generated or synthesized rather than being collected from real-world events or interactions.
- Context:
- It can (often) be used to Machine Learning Model Training (e.g. when real-world data is scarce, sensitive, or difficult to obtain).
- It can be generated using various techniques such as simulation, data augmentation, or generative adversarial networks (GANs).
- It can help in addressing issues like data privacy, data bias, and data scarcity in machine learning.
- It can be tailored to include specific features or scenarios that are not present in the available real-world data.
- It can improve the robustness and generalizability of machine learning models.
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- Example(s):
- a Synthetic LLM Training Dataset, ...
- a Synthetic Images Dataset, generated for training computer vision systems.
- a Simulated Financial Transactions Dataset, for fraud detection models.
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- Counter-Example(s):
- Real-World Training Dataset, such as actual customer transaction data in a retail database.
- See: Data Generation, Machine Learning, Artificial Intelligence, Data Privacy, Data Security.