Quantifying the Effects of Correlated Covariates on Variable Importance Estimates from Random Forests

QUANTIFYING THE EFFECTS OF CORRELATED COVARIATES ON VARIABLE IMPORTANCE ESTIMATES FROM RANDOM FORESTS By Ryan Vincent Kinies A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University. Virginia Commonwealth University, 2006 Major Director: Kellie J. Archer, Ph.D. Assistant Professor, Department of Biostatistics Recent advances in computing technology have lead to the development of algorithmic modeling techniques. These methods can be used to analyze data which are difficult to analyze using traditional statistical models. This study examined the effectiveness of variable importance estimates from the random forest algorithm in identifying the true predictor among a large number of candidate predictors. A simulation study was conducted using twenty different levels of association among the independent variables and seven different levels of association between the true predictor and the