Active Learning is a suggested strategy for improving the accuracy of machine learning algorithms by enabling them to designate test zones.
The algorithm may choose a new point x at any time, examine
the outcome, and add the new (x, y) pair to its training base.
Neural networks, prediction functions, and clustering
functions have all benefited from it.
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