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Active Learning

When you have a large amount of data but lack labels for the entire dataset, you can design a system that queries a user for labels on-the-fly. Active learning is semi-supervised and is useful for when the cost of acquiring labels is high.

You may have seen real world examples of active learning in software such as Facebook, where a user is asked to provide the tag for the photo. Negative tweet recognition on Twitter is another example of collecting a label - positive vs. negative tweet - and storing the data for training a model.

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