select_control: Set Parameters for Selection Step of Fuzzy Forests

In fuzzyforest: Fuzzy Forests

Description

Creates selection_control object for controlling how feature selection will be carried out after features from different modules have been combined.

Usage

select_control(drop_fraction = 0.25, number_selected = 5,
  mtry_factor = 1, min_ntree = 500, ntree_factor = 1)

Arguments

drop_fraction	A number between 0 and 1. Percentage of features dropped at each iteration.
number_selected	A positive number. Number of features that will be selected by fuzzyforests.
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.
min_ntree	Minimum number of trees grown in each random forest.
ntree_factor	A number greater than 1. ntree for each random forest is ntree_factor times the number of features. For each random forest, ntree is set to max(min_ntree, ntree_factor*p).

Value

An object of type selection_control.

Note

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

References

Conn, D., Ngun, T., Ramirez C.M., Li, G. (2019). "Fuzzy Forests: Extending Random Forest Feature Selection for Correlated, High-Dimensional Data." Journal of Statistical Software, 91(9). doi: 10.18637/jss.v091.i09

Examples

drop_fraction <- .25
number_selected <- 10
mtry_factor <- 1
min_ntree <- 5000
ntree_factor <- 5
select_params <- select_control(drop_fraction=drop_fraction,
                                number_selected=number_selected,
                                mtry_factor=mtry_factor,
                                min_ntree=min_ntree,
                                ntree_factor=ntree_factor)

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