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FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees

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  • Phillips, Nathaniel D.
  • Neth, Hansjörg
  • Woike, Jan K.
  • Gaissmaier, Wolfgang

Abstract

Fast-and-frugal trees (FFTs) are simple algorithms that facilitate efficient and accurate decisions based on limited information. But despite their successful use in many applied domains, there is no widely available toolbox that allows anyone to easily create, visualize, and evaluate FFTs. We fill this gap by introducing the R package FFTrees. In this paper, we explain how FFTs work, introduce a new class of algorithms called fan for constructing FFTs, and provide a tutorial for using the FFTreespackage. We then conduct a simulation across ten real-world datasets to test how well FFTs created by FFTreescan predictdata. Simulation results show that FFTs created by FFTreescan predict data as well as popular classification algorithms such as regression and random forests, while remaining simple enough for anyone to understand and use.

Suggested Citation

  • Phillips, Nathaniel D. & Neth, Hansjörg & Woike, Jan K. & Gaissmaier, Wolfgang, 2017. "FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 12(4), pages 344-368.
    • Handle: RePEc:zbw:espost:201523
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    References listed on IDEAS

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    1. John C. Hershey & Randall D. Cebul & Sankey V. Williams, 1986. "Clinical Guidelines for Using Two Dichotomous Tests," Medical Decision Making, , vol. 6(2), pages 68-78, June.
    2. Bonnie K. Ray & Ruey S. Tsay, 2002. "Bayesian methods for change‐point detection in long‐range dependent processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 23(6), pages 687-705, November.
    3. Luan, Shenghua & Reb, Jochen, 2017. "Fast-and-frugal trees as noncompensatory models of performance-based personnel decisions," Organizational Behavior and Human Decision Processes, Elsevier, vol. 141(C), pages 29-42.
    4. repec:cup:judgdm:v:12:y:2017:i:4:p:344-368 is not listed on IDEAS
    5. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    6. Nathaniel D. Phillips & Hansjörg Neth & Jan K. Woike & Wolfgang Gaissmaier, 2017. "FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 12(4), pages 344-368, July.
    7. Katsikopoulos, Konstantinos V., 2016. "On the role of psychological heuristics in operational research; and a demonstration in military stability operationsAuthor-Name: Keller, Niklas," European Journal of Operational Research, Elsevier, vol. 249(3), pages 1063-1073.
    8. Kim, Jae-Young, 2000. "Detection of change in persistence of a linear time series," Journal of Econometrics, Elsevier, vol. 95(1), pages 97-116, March.
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    Cited by:

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    3. Gadzinski, Gregory & Castello, Alessio, 2020. "Fast and Frugal heuristics augmented: When machine learning quantifies Bayesian uncertainty," Journal of Behavioral and Experimental Finance, Elsevier, vol. 26(C).
    4. Lutz Bornmann & Julian N. Marewski, 2019. "Heuristics as conceptual lens for understanding and studying the usage of bibliometrics in research evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 419-459, August.
    5. Bornmann, Lutz & Ganser, Christian & Tekles, Alexander, 2022. "Simulation of the h index use at university departments within the bibliometrics-based heuristics framework: Can the indicator be used to compare individual researchers?," Journal of Informetrics, Elsevier, vol. 16(1).
    6. Jan Niederreiter, 2023. "Broadening Economics in the Era of Artificial Intelligence and Experimental Evidence," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 9(1), pages 265-294, March.
    7. repec:cup:judgdm:v:17:y:2022:i:3:p:598-627 is not listed on IDEAS
    8. Philipp Lorenz-Spreen & Stephan Lewandowsky & Cass R. Sunstein & Ralph Hertwig, 2020. "How behavioural sciences can promote truth, autonomy and democratic discourse online," Nature Human Behaviour, Nature, vol. 4(11), pages 1102-1109, November.

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