Guided Random Forest in the RRF Package

Understand

Random Forest (RF) is a powerful supervised learner and has been popularly used in many applications such as bioinformatics.

• In this work we propose the guided random forest (GRF) for feature selection.
• Similar to a feature selection method called guided regularized random forest (GRRF), GRF is built using the importance scores from an ordinary RF.
• However, the trees in GRRF are built sequentially, are highly correlated and do not allow for parallel computing, while the trees in GRF are built independently and can be implemented in parallel.