Informally, a fast and frugal decision tree (FFT) is a set of rules for making decisions based on very little information (usually 1 to 5 pieces). Formally, a FFT is a decision tree has at least one exit on each level of the tree (Martignon, Vitouch, Takezawa, & Forster, 2003). 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). 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 FFTs like the one below.
bcancer.fft <- fft(diagnosis ~.,
data = breastcancer
)
plot(bcancer.fft,
description = "Breast Cancer",
decision.names = c("Absent", "Present"))
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.
The package contains several datasets taken from the UCI Machine Learning Repository.
heartdisease
– patients suspected of having heart disease sourcebreastcancer
– patients suspected of having breast cancer sourcetitanic
– records of which passengers on the Titanic survivedforestfires
– forest fire statistics sourcewine
– ratings of wine quality sourceincome
– Census data from > 30,000 US residents sourcebank
– Bank marketing dataset sourceTo learn more about the package, click the following guides:
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.
Martignon, L., Vitouch, O., Takezawa, M., & Forster, M. R. (2003). Naive and yet enlightened: From natural frequencies to fast and frugal decision trees. Thinking: Psychological Perspective on Reasoning, Judgment, and Decision Making, 189–211.