Fast-and-frugal trees for decision-making

<jats:p>Fast-and-frugal trees for classification/decision are at the intersection of three families of models: lexicographic, linear and tree-based. We briefly examine the classification performance of simple models when making inferences out of sample, in 11 medical data sets in terms of Receiver Operating Characteristics diagrams and predictive accuracy. The heuristic approaches, Naïve Bayes and fast- and-frugal trees, outperform models that are normatively optimal when fitting data. The success of fast-and-frugal trees lies in their ecological rationality: their construction exploits the structure of information in the data sets. The tool ARBOR, a digital learning tool, which is a plug-in to the freely available data-science education software CODAP can be used for constructing and interpreting fast- and-frugal classification and decision trees. This paper is an abridged version of work by Woike, Hoffrage &amp; Martignon on the integration of classification and decision models into a common framework (Woike, Hoffrage &amp; Martignon, 2017).</jats:p>