Fuzzy forests, a new algorithm based on random forests, is designed to reduce the bias seen in random forest feature selection caused by the presence of correlated features. Fuzzy forests uses recursive feature elimination random forests to select features from separate blocks of correlated features where the correlation within each block of features is high and the correlation between blocks of features is low. One final random forest is fit using the surviving features. This package fits random forests using the 'randomForest' package and allows for easy use of 'WGCNA' to split features into distinct blocks.
|Author||Daniel Conn [aut, cre], Tuck Ngun [aut], Christina M. Ramirez [aut]|
|Date of publication||2016-06-16 05:28:03|
|Maintainer||Daniel Conn <email@example.com>|
ctg: Cardiotocography Data Set
example_ff: Fuzzy Forest Example
ff: Fits fuzzy forest algorithm.
fuzzyforest: fuzzyforest: an implementation of the fuzzy forest algorithm...
fuzzy_forest: Fuzzy Forest Object
iterative_RF: Fits iterative random forest algorithm.
Liver_Expr: Liver Expression Data from Female Mice
modplot: Plots relative importance of modules.
predict.fuzzy_forest: Predict method for fuzzy_forest object. Obtains predictions...
print.fuzzy_forest: Print fuzzy_forest object. Prints output from fuzzy forests...
screen_control: Set Parameters for Screening Step of Fuzzy Forests
select_control: Set Parameters for Selection Step of Fuzzy Forests
select_RF: Carries out the selection step of fuzzyforest algorithm.
wff: Fits WGCNA based fuzzy forest algorithm.
WGCNA_control: Set Parameters for WGCNA Step of Fuzzy Forests
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