uniqueByEquivalenceClass

the number of features in the original data. This can be
obtained from a forest object via <code>forest$params$paramList$p</code>.

a list of projections from intermediate
steps of the <code><a rd-options="" href="/link/FeatureImportance?package=rerf&version=2.0.4" data-mini-rdoc="rerf::FeatureImportance">FeatureImportance</a></code> function.

unique.projections

This function finds the projections that are equivalent via a 180
degree rotation and removes the duplicates.

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

Randomer Forest

Benjamin Falk

Jaewon Chung

James Browne

Tyler Tomita

Joshua Vogelstein

uniqueByEquivalenceClass function

a list of projections from intermediate
steps of the <code><a rd-options='' href='FeatureImportance'>FeatureImportance</a></code> function.

uniqueByEquivalenceClass: Remove unique projections that are equivalent due to a rotation of 180 degrees.

Description

Usage

Arguments

Value

See Also