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 decisions when designing and implementing active ML algorithms.
In this chapter, you will gain skills that will equip you to design and deploy query strategies that extract maximum value from labeling efforts. You will gain intuition for matching...
