Summary

In this introductory chapter, we covered the fundamentals of active ML and how it contrasts with passive learning approaches.

You learned what active learning is and its goal of maximizing predictive performance with fewer labeled training examples. We discussed the core components of an active learning system: the unlabeled data pool, query strategy, machine learning model, and the oracle labeler.

You now understand the difference between membership query synthesis, stream-based sampling, and pool-based sampling scenarios. We compared active and passive learning, highlighting the benefits of an interactive, iterative approach in active learning.

Importantly, you now know that active learning can produce models with equal or greater accuracy while requiring far less labeled training data. This is critical for reducing the costs of modeling, as labeling is often the most expensive component.

The skills you gained in this introduction will equip you to determine when...