# Create an FFT from the heartdisease dataset heart.fft <- FFTrees(formula = diagnosis ~., data = heartdisease) # Plot the best training tree plot(heart.fft, main = "Heart Disease", decision.names = c("Absent", "Present"))
A fast and frugal tree (FFT) is a set of hierarchical rules for making decisions based on very little information (usually 4 or fewer). For example, the tree above uses data from to decide whether a patient truly has heart disease or not based on up to 3 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 preferable to more complex decision strategies (such as Logistic Regression) because they rarely over-fit data (Gigerenzer & Brighton, 2009) and are easy to interpret and implement 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. The main function in the package is
FFTrees() which takes formula
formula and dataset
data arguments and returns several FFTs which attempt to classify training cases into criterion classes. For additional details and examples, check out the vignettes below:
The package contains several datasets taken from the UCI Machine Learning Repository that you can use to play around with FFTrees.
heartdisease– patients suspected of having heart disease source
breastcancer– patients suspected of having breast cancer source
titanic– records of which passengers on the Titanic survived
forestfires– forest fire statistics source
wine– ratings of wine quality source
income– Census data from > 30,000 US residents source
bank– Bank marketing dataset source
This package is constantly being updated. The latest developer version is always at https://github.com/ndphillips/FFTrees. 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.firstname.lastname@example.org
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.