A Transparent, Simple AI Tool for Constructing Efficient and Robust Fast and Frugal Trees for Classification Under Risk
Laura F. Martignon, Joachim Engel, Tim Erickson, Eeps Media · Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. Proceedings of the Eleventh International Conference on Teaching Statistics · 2022
Artificial Intelligence (AI) has produced extremely efficient and effective classification and decision “machines” that learn from given data sets and generalize well to unknown data. These are mostly celebrated tools produced by methodologies of machine learning. There is a drawback, though, namely their lack of transparency in construction. Agents often ignore the construction steps and use them as black-box algorithms. We exhibit simple and transparent steps for creating robust and yet simple heuristics for classification based on the AI tool ARBOR. We also claim that these transparent classifiers compete well against powerful machines, especially when training sets are small.