iterative_RF {fuzzyforest}

Fits iterative random forest algorithm.

Description

Fits iterative random forest algorithm. Returns data.frame with variable importances and top rated features. For now this is an internal function that I've used to explore how recursive feature elimination works in simulations. It may be exported at a later time.

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(\sqrt{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 and AMFAR 8721SC.