Search

Previous studies have shown that the predictive accuracy of fast and frugal decision trees (FFTs) is comparable todecision trees generated by machine-learning (Martignon et al., 2008). FFTs are thought to be useful decision tools that arecognitively plausible to internalise, as opposed to complex machine-learning algorithms. Nonetheless, there seems to be a lackof behavioural studies in the literature to support such a claim. In this between-group experiment, we examined the human useof an FFT versus a C4.5 algorithm tree when completing a car evaluation task. Participants had to learn the rules of their giventree before making evaluations based on their memory. Preliminary results show that FFTs may indeed be easier to use, evenwhen the number of cues for both trees are the same. Interestingly, participants who were successful in using the C4.5 treeexhibited tree pruning strategies, resulting in a heuristic similar to an FFT.