Active learning is a form of supervised machine learning in which a learning algorithm is able to interactively query the user (or some other information source) to obtain the desired outputs at new data points. In statistics literature it is sometimes also called optimal experimental design.[1][2]
There are situations in which unlabeled data is abundant
but manually labeling it is expensive. In such a scenario,
learning algorithms can actively query the user/teacher for
labels. This type of iterative supervised learning is called
active learning. Since the learner chooses the examples, the
number of examples to learn a concept can often be much lower than
the number required in normal supervised learning. With this
approach, there is a risk that the algorithm be overwhelmed by
uninformative examples.
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