Create, visualise, and test fast and frugal decision trees (FFTrees). FFTrees are very simple decision trees for classifying cases (i.e.; breast cancer patients) into one of two classes (e.g.; no cancer vs. true cancer) based on a small number of cues (e.g.; test results). FFTrees can be preferable to more complex algorithms because they are easy to communicate, require very little information, and are robust against overfitting.
An R package to create and visualize fast and frugal decision trees (FFTrees)
Trees can now use the same cue multiple times within a tree. To do this, set
rank.method = "c" and
repeat.cues = TRUE.
FFTrees()now supports a single predictor (e.g.;
formula = diagnosis ~ age) which previously did not work.
Streamlined code to improve cohesion between functions. This may cause issues with FFTrees objects created with earlier versions of the package. They will need to be re-created.
print.FFTrees() method to see important info about an FFTrees object in matrix format.
Training and testing statistics are now always in seperate objects (e.g.;
data$test) to avoid confusion.
predict.FFTrees()now works much better by passing a new dataset (
data.test) as a test dataset for an existing FFTrees object.
layoutare now reset after running
treeto conform to blog posts.
predict.FFTrees()now works better with
fft label to
FFTrees throughout the package to avoid confusion with fast fourier transform. Thus, the main tree building function is now
FFTrees() and the new tree object class is