iterative_RF: Fits iterative random forest algorithm.

In OHDSI/FuzzyForest: Fuzzy Forests

Description

Fits iterative random forest algorithm. Returns data.frame with variable importances and top rated features.

Usage

iterative_RF(X, y, drop_fraction, keep_fraction, mtry_factor,
  ntree_factor = 10, min_ntree = 5000, num_processors = 1, nodesize)

Arguments

X	A data.frame. Each column corresponds to a feature vectors.
y	Response vector.
drop_fraction	A number between 0 and 1. Percentage of features dropped at each iteration.
keep_fraction	A number between 0 and 1. Proportion features from each module to retain at screening step.
mtry_factor	A positive number. Mtry for each random forest is set to ceiling(√{p}mtry_factor) where p is the current number of features.
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.

OHDSI/FuzzyForest documentation built on May 7, 2019, 8:26 p.m.