Machine learning is an increasingly used computational tool within human-computer interaction research. While most researchers currently utilize an iterative approach to refining classifier models and performance, we propose that ensemble classification techniques may be a viable and even preferable alternative.

In ensemble learning, algorithms combine multiple classifiers to build one that is superior to its components. We designed and developed a new interactive visualization system that presents a graphical view of confusion matrices to help users under-stand relative merits of various classifiers.