Overview: Creating FFTs with FFTrees

Fast-and-Frugal Trees (FFTs)

A fast-and-frugal tree (FFT) (Martignon et al., 2003) is a set of hierarchical rules for solving binary classification tasks based on very little pieces of information (usually using 4 or fewer cues). In contrast to more complex decision trees, each node of an FFT has exactly two branches. A branch can either contain another cue (i.e., ask another question) or lead to an exit (i.e., yield a decision or prediction outcome).
Each non-final node of an FFT has one exit branch and the final node has two exit branches.

FFTs are simple and effective decision strategies that use minimal information for making decisions in binary classification problems (Gigerenzer et al., 1999; see Gigerenzer & Todd, 1999). FFTs are often preferable to more complex decision strategies (such as logistic regression, LR) because they rarely over-fit data (Gigerenzer & Brighton, 2009) and are easy to interpret, implement, and communicate in real-world settings (Marewski & Gigerenzer, 2012). FFTs have been designed to tackle many real world tasks from making fast decisions in emergency rooms (Green & Mehr, 1997) to detecting depression (Jenny et al., 2013).

Whereas their performance and success are empirical questions, a key theoretical advantage of FFTs is their transparency to decision makers and anyone aiming to understand and evaluate the details of an algorithm. In the words of Burton et al. (2020), “human users could interpret, justify, control, and interact with a fast-and-frugal decision aid” (p. 229).

Using the FFTrees package

The FFTrees package makes it easy to produce, display, and evaluate FFTs (Phillips et al., 2017). The package’s main function is FFTrees() which takes formula formula and dataset data arguments and returns several FFTs that attempt to classify training cases into criterion classes. The FFTs created can then be used to predict new data to cross-validate their performance.

Here is an example of using the main FFTrees() function to fit FFTs to heart.train data:

# Create a fast-and-frugal tree (FFT) predicting heart disease:
heart.fft <- FFTrees(formula = diagnosis ~.,
                     data = heart.train,
                     data.test = heart.test,
                     main = "Heart Disease",
                     decision.labels = c("Healthy", "Diseased"))

The resulting FFTrees object heart.fft contains 7 FFTs that were fitted to the heart.test data. To evaluate a tree’s predictive performance, we compare its predictions for the un-trained heart.test data with their true criterion values. Here is how we can apply the best training FFT to the heart.test data:

# Visualize predictive performance:
plot(heart.fft, data = "test")

A fast-and-frugal tree (FFT) to predict heart disease status.

Getting started

To start using the FFTrees package, we recommend studying the Tutorial: Creating FFTs for heart disease. The tutorial illustrates the basics steps of creating, visualizing, and evaluating fast-and-frugal trees (FFTs). The scientific background of FFTs and the development of FFTrees are described in Phillips et al. (2017) (doi 10.1017/S1930297500006239 | html | PDF). The following vignettes provide details on related topics and corresponding examples.

Citing FFTrees

We had a lot of fun creating FFTrees and hope you like it too! For an accessible introduction to FFTs, we recommend reading our article in the journal Judgment and Decision Making (2017), entitled FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees (available in html | PDF ).

Citation (in APA format):

• Phillips, N. D., Neth, H., Woike, J. K. & Gaissmaier, W. (2017). FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees. Judgment and Decision Making, 12 (4), 344–368. doi 10.1017/S1930297500006239

When using FFTrees in your own work, please cite our article and spread the word, so that we can continue developing the package.

BibTeX Citation:

@article{FFTrees,
 title = {FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees},
 author = {Phillips, Nathaniel D and Neth, Hansjörg and Woike, Jan K and Gaissmaier, Wolfgang},
 year = 2017, 
 journal = {Judgment and Decision Making},
 volume = 12,
 number = 4,
 pages = {344--368},
 url = {https://journal.sjdm.org/17/17217/jdm17217.pdf},
 doi = {10.1017/S1930297500006239}
}

Contact

• The latest release of FFTrees is available at https://CRAN.R-project.org/package=FFTrees.

• The latest developer version is available at https://github.com/ndphillips/FFTrees.

• For comments, tips, and bug reports, please post at https://github.com/ndphillips/FFTrees/issues or contact Nathaniel at [email protected] or https://www.linkedin.com/in/nathanieldphillips/.

Bibliography