In chapters 3 and 4, you learned how to identify where your model is uncertain (what your model knows it doesn’t know) and what is missing from your model (what your model doesn’t know that it doesn’t know). In this chapter, you learn how to combine these techniques into a comprehensive active learning strategy. You also learn how to use transfer learning to adapt your models to predict which items to sample.
This section explores ways to combine all the active learning techniques that you have learned up to this point so that you can use them effectively them for your particular use cases. You will also learn one new active learning strategy: expected error reduction, which combines principles of uncertainty sampling and diversity sampling. Recall from chapter 1 that an ideal strategy for active learning tries to sample items that are near the decision boundary but are distant from one another, as shown in figure 5.1.