select_RF: Carries out the selection step of fuzzyforest algorithm.

In fuzzyforest: Fuzzy Forests

Description

Carries out the selection step of fuzzyforest algorithm. Returns data.frame with variable importances and top rated features.

Usage

select_RF(X, y, drop_fraction, number_selected, mtry_factor, ntree_factor,
  min_ntree, num_processors, nodesize)

Arguments

X	A data.frame. Each column corresponds to a feature vectors. Could include additional covariates not a part of the original modules.
y	Response vector.
drop_fraction	A number between 0 and 1. Percentage of features dropped at each iteration.
number_selected	Number of features selected by fuzzyforest.
mtry_factor	In the case of regression, mtry is set to ceiling(√(p)*mtry_factor). In the case of classification, mtry is set to ceiling((p/3)*mtry_factor). If either of these numbers is greater than p, mtry is set to p.
ntree_factor	A number greater than 1. ntree for each random is ntree_factor times the number of features. For each random forest, ntree is set to max(min_ntree, ntree_factor*p).
min_ntree	Minimum number of trees grown in each random forest.
num_processors	Number of processors used to fit random forests.
nodesize	Minimum nodesize

Value

A data.frame with the top ranked features.

Note

This work was partially funded by NSF IIS 1251151 and AMFAR 8721SC.

fuzzyforest documentation built on March 25, 2020, 5:09 p.m.