The fft package

Nathaniel Phillips

2016-07-19

Overview

The purpose of this package is to produce, compare, and display Fast and Frugal Decision Trees (FFTs) like the one below. FFTs 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).

The main function in the package is fft() which takes a formula formula and a training dataset data arguments and returns several FFTs which attempt to classify training cases into criterion classes. The fft() function returns a list object with the “fft” class which can then be passed to other functions such as plot() (which plots the FFTs), and predict() which applies an existing set of FFTs to new datasets.

Datasets

The package contains several datasets taken from the UCI Machine Learning Repository.

Guides

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

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.

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