In 2003, Fails and Olsen described an approach to enabling users to build simple image classifiers, in which users could iteratively improve a model through edits to its training set. They term this approach interactive machine learning, in contrast to more conventional approaches to machine learning in which a person focuses their effort on changing the learning algorithm or features rather than the training data. Interactive machine learning (IML) therefore seems like a potentially good fit for musical instrument building, given the observations above around the ease with which instrument builders may be able to provide new training examples, and the importance of enabling designers to iteratively change the training set and experiment with the resulting model variants.