An Application of the Bootstrap 632+ Rule to Ecological Data

Abstract

We applied the novel bootstrap 632 rule to choose tree-based classifiers trained for modeling the risk of parasite presence in a host population of ungulates. The method is designed to control overfitting: compact classification trees (CART) are selected using a nonlinear combination of the resubstitution error and the standard bootstrap error estimate. Model selection based on the 632 rule offers a gain over cross-validation for CART models. The tree classifier selected by the new rule for this application favourably compared with standard multivariate GLIM models