Active Machine Learning with Python

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Designing Query Strategy Frameworks

Query strategies act as the engine that drives active ML and determines which data points get selected for labeling. In this chapter, we aim to provide a comprehensive and detailed explanation of the most widely used and highly effective query strategy frameworks that are employed in active ML. These frameworks play a crucial role in the field of active ML, aiding in selecting informative and representative data points for labeling. The strategies that we will delve into include uncertainty sampling, query-by-committee, expected model change (EMC), expected error reduction (EER), and density-weighted methods. By thoroughly understanding these frameworks and the underlying principles, you can make informed ...