Analysis of a Design Pattern for Teaching with Features and Labels
This work addresses the challenge of efficient teaching in interactive machine learning systems, but it is incremental as it builds on existing teaching patterns and linear learning algorithms.
The paper tackles the problem of teaching machines to classify objects using features and labels by introducing the Error-Driven-Featuring design pattern, which prioritizes introducing features only when necessary, and analyzes its risks and benefits through protocols, examples, and bounds on effort for optimal teaching with linear learners.
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).