Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology.

ABSTRACT: Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological data sets, there is limited guidance on variable selection methods for RF modeling. Typically, either a preselected set of predictor variables are used or stepwise procedures are employed which iteratively remove variables according to their importance measures. This paper investigates the application of variable selection methods to RF models for predicting probable biological stream condition. Our motivating data set consists of the good/poor condition of n = 1365 stream survey sites from the 2008/2009 National Rivers and Stream Assessment, and a large set (p = 212) of landscape features from the StreamCat

SUBMITTER: Fox EW 

PROVIDER: S-EPMC6049094 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature