  We study the task of teaching a machine to classify objects using features
and labels. We introduce the Error-Driven-Featuring design pattern for teaching
using features and labels in which a teacher prefers to introduce features only
if they are needed. We analyze the potential risks and benefits of this
teaching pattern through the use of teaching protocols, illustrative examples,
and by providing bounds on the effort required for an optimal machine teacher
using a linear learning algorithm, the most commonly used type of learners in
interactive machine learning systems. Our analysis provides a deeper
understanding of potential trade-offs of using different learning algorithms
and between the effort required for featuring (creating new features) and
labeling (providing labels for objects).
