Generate, Visualise, and Evaluate Fast-and-Frugal Decision Trees

Create, visualize, and test fast-and-frugal decision trees (FFTs). FFTs are very simple decision trees for binary classification problems. FFTs 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)

Package updates

1.1.7

• Trees can now use the same cue multiple times within a tree. To do this, set rank.method = "c" and repeat.cues = TRUE.

• Bug-fixes

  • You can (and should!) now have a column of NAs for the criterion in test datasets to represent data where the criterion is unknown.
  • FFTrees() now supports a single predictor (e.g.; formula = diagnosis ~ age) which previously did not work.

1.1.6

• 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.

• Updated, clearer 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$train, data$test) to avoid confusion.

• Bug-fixes

  • predict.FFTrees() now works much better by passing a new dataset (data.test) as a test dataset for an existing FFTrees object.

1.1.5

• Bug-fixes
  • Plotting parameters mar and layout are now reset after running plot.FFTrees()

1.1.4

• Bug-fixes

  • Plotting no longer fails when there is only one branch in the tree.
  • Changed which.tree argument in plot.FFTrees() to tree to conform to blog posts.
  • predict.FFTrees() now works better with tibble inputs.

• Changed the 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 FFTrees