We applied the novel bootstrap 632+ rule to choose tree-based classifiers trained for modelling the risk of parasite presence in the 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 bootsrap 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
An Application of the Bootstrap 632+ Rule to Ecological Data
Furlanello, Cesare;Merler, Stefano;Rizzoli, Annamaria
1998-01-01
Abstract
We applied the novel bootstrap 632+ rule to choose tree-based classifiers trained for modelling the risk of parasite presence in the 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 bootsrap 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 modelsFile in questo prodotto:
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