The Random Forests algorithm (RF) implemented in R via the randomForest package, is quite possibly the most data driven algorithm in existense today. Its high popularity stems from its excellent overall performance in prediction. However, due to its statistical complexity it is in general difficult to understand variable or feature contribution to the prediction. In addition, due to the variable random selection that occurs internally on each fully grown tree, RFs are not truly capturing higher order interaction effects. The exception to this are 2 way interactions where the effect is indeed captured.

Nonetheless, there have been efforts to alleviate such difficulties, and one such noble effort has been implemented and described in the R package forestFloor.

This package currently uses the results of forestFloor, hence it is advised that the user goes through forestFloor first, in order to better understand its theory and usage. Equipped with that knowledge, the user can then more easily transition to vis2rfi.

vis2rfi extracts Random Forest feature contributions that are computed in forestFloor, and combines them in order to do specifically one thing :

• Create 3 dimensional objects and/or surfaces that aim to describe the interaction effect of two variables on the predicted outcome.

The package’s name is shorthand for Visualising 2 Way Random Forest Interactions.

forestFloor implements a function for the same purpose, namely forestFloor::show3d, which is using rgl to create the visualisation. Here the approach taken is different (explained below), while the plotting tool is plotly.

Further details can be found via the articles tab above.