Decision trees are commonly used in machine learning, especially in classification. Compared to other types of models, such as neural networks, they offer a rather intuitive interpretation and they are transparent in application. Visual analytics can be used to build and refine machine learning models. For decision trees, a visual interface based on parallel coordinate plots may offer a suitable approach for analysts to evaluate and improve their models.
In this project, you plan to build an interactive visualization that enables the construction and evaluation of a particular class of decision trees, namely Fast-and-Frugal Trees. Fast-and-Frugal Trees pose some additional constraints on the tree structure, which yo can exploit in the design of the visual interface. The goal is to allow a closer integration of the human analyst in the development and evaluation of the classifiers. In particular, the visual interface will incorporate a diverse set of interactions, which are relevant at the different steps in model evaluation and refinement.
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- Nathaniel D. Phillips, Hansörg Neth, Jan K. Woike, and Wolfgang Gaissmaier (2017) FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees, In: Judgment and Decision Making 12(4) pp. 344–368