Overview of the FFTrees Package

Nathaniel Phillips

2016-08-23

Fast and Frugal Trees (FFTrees)

What is a Fast and Frugal Tree (FFTree)? A FFTree is a set of rules for making decisions based on very little information (usually 5 or fewer). For example, the tree above uses data from breast cancer patients to decide whether a woman truly has breast cancer or not based on only two pieces of information.

FFTrees are simple, transparent decision strategies that use minimal information to make decisions (see Gigerenzer & Todd, 1999; Gigerenzer, Czerlinski, & Martignon, 1999). They are frequently prefereable to more complex decision strategies (such as Logistic Regression) because they rarely overfit data (Gigerenzer & Brighton, 2009) and are easy to interpret and impliment in real-world decision tasks (Marewski & Gigerenzer, 2012). They have been used in real world tasks from detecting depression (Jenny, Pachur, Williams, Becker, & Margraf, 2013), to making fast decisions in emergency rooms (Green & Mehr, 1997).

The purpose of the FFTrees package is to produce, compare, and display FFTrees. The main function in the package is FFTrees() which takes formula formula and dataset data arguments and returns several FFTrees which attempt to classify training cases into criterion classes. For additional details and examples, check out the vignettes below:

Vignettes

To learn more about the package, click the following vignettes:

Datasets

The package contains several datasets taken from the UCI Machine Learning Repository that you can use to play around with FFTs.

Contact

This package is constantly being updated. For comments, tips, additional references, and bug reports, please add an issue at https://github.com/ndphillips/FFTrees/issues or email me at Nathaniel.D.Phillips.is@gmail.com.

Bibliography

Gigerenzer, G., & Brighton, H. (2009). Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science, 1(1), 107–143.

Gigerenzer, G., & Todd, P. M. (1999). Fast and frugal heuristics: The adaptive toolbox. In Simple heuristics that make us smart (pp. 3–34). Oxford University Press.

Gigerenzer, G., Czerlinski, J., & Martignon, L. (1999). How good are fast and frugal heuristics? In Decision science and technology (pp. 81–103). Springer.

Green, L., & Mehr, D. R. (1997). What alters physicians’ decisions to admit to the coronary care unit? Journal of Family Practice, 45(3), 219–226.

Jenny, M. A., Pachur, T., Williams, S. L., Becker, E., & Margraf, J. (2013). Simple rules for detecting depression. Journal of Applied Research in Memory and Cognition, 2(3), 149–157.

Marewski, J. N., & Gigerenzer, G. (2012). Heuristic decision making in medicine. Dialogues Clin Neurosci, 14(1), 77–89.