Ensemble Models of Rank-Based Trees with Extracted Decision Rules

Documentation for package ‘ranktreeEnsemble’ version 0.22

ranktreeEnsemble-package	Ensemble Models of Rank-Based Trees for Single Sample Classification with Interpretable Rules
extract.rules	Extract Interpretable Decision Rules from a Random Forest Model
importance	Variable Importance Index for Each Predictor
pair	Transform Continuous Variables into Ranked Binary Pairs
predict	Prediction or Extract Predicted Values for Random Forest, Random Forest Rule or Boosting Models
ranktreeEnsemble	Ensemble Models of Rank-Based Trees for Single Sample Classification with Interpretable Rules
rboost	Generalized Boosted Modeling via Rank-Based Trees for Single Sample Classification with Gene Expression Profiles
rforest	Random Forest via Rank-Based Trees for Single Sample Classification with Gene Expression Profiles
rforest.tree	Random Forest via Rank-Based Trees for Single Sample Classification with Gene Expression Profiles
select.rules	Select Decision Rules to Achieve Higher Prediction Accuracy
tnbc	Gene expression profiles in triple-negative breast cancer cell