RunFeatureImportance

a single tree from a trained RerF model with argument store.impurity = TRUE.

tree

a list of all of the unique split projections used in the RerF model.

unique.projections

Computes feature importance of every unique feature used to make a split in a single tree.

R-RerF (aka Randomer Forest (RerF) or Random Projection
Forests) is an algorithm developed by Tomita (2016) <arXiv:1506.03410v2>
which is similar to Random Forest - Random Combination (Forest-RC)
developed by Breiman (2001) <doi:10.1023/A:1010933404324>. Random
Forests create axis-parallel, or orthogonal trees. That is, the feature
space is recursively split along directions parallel to the axes of the
feature space. Thus, in cases in which the classes seem inseparable
along any single dimension, Random Forests may be suboptimal. To
address this, Breiman also proposed and characterized Forest-RC, which
uses linear combinations of coordinates rather than individual
coordinates, to split along. This package, 'rerf', implements RerF
which is similar to Forest-RC. The difference between the two
algorithms is where the random linear combinations occur: Forest-RC
combines features at the per tree level whereas RerF takes linear
combinations of coordinates at every node in the tree.

Jesse Patsolic

rerf

RunFeatureImportance: Compute Feature Importance of a single RerF tree

Description

Usage

Arguments

Value