select_RF: Carries out selection step of fuzzyforest algorithm.

Description Usage Arguments Value Note

View source: R/iterative_RF.R

Description

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

Usage

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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.


OHDSI/FuzzyForest documentation built on May 9, 2017, 3:26 p.m.