Teaching about decision trees for classification problems

Abstract

In times of big data tree-based algorithms are an important method of machine learning which supports decision making, e.g., in medicine, finance, public policy and many more. Trees are a versatile method to represent decision processes that mirror human decision-making more closely than sophisticated traditional statistical methods like multivariate regression or neural networks. We introduce and illustrate the tool ARBOR, a digital learning tool which is a plug-in to the freely available data science education software CODAP. It is designed to critically appreciate and explore the steps of automatically generated decision trees.