Generating training data for machine learning models
A technology for machine learning models and training data sets, applied in the field of generating training data for machine learning models
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[0014] Various methods are disclosed for generating additional data for training machine learning models to complement small or noisy datasets that may not be sufficient to train machine learning models. When only small datasets are available to train machine learning models, data scientists can try to expand their datasets by collecting more data. However, this is not always possible. For example, datasets representing infrequently occurring events can only be supplemented by waiting an extended period of time for additional occurrences of the event. As another example, a dataset based at least in part on a small population size (eg, data representing a small group of people) cannot meaningfully scale by simply adding more members to the population.
[0015]Additional records can be added to these small datasets, but there are disadvantages. For example, one may have to wait a significant amount of time to collect enough data related to infrequent events in order to have a ...
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