Abstract
We present a cross-validated bagging scheme in the context of partitioning algorithms. To explore the benefits of the various bagging scheme, we compare via simulations the predictive ability of single Classification and Regression (CART) Tree with several previously suggested bagging schemes and with our proposed approach. Additionally, a variable importance measure is explained and illustrated.
Disciplines
Statistical Methodology | Statistical Models | Statistical Theory
Suggested Citation
Molinaro, Annette M. and van der Laan, Mark J., "Cross-Validating and Bagging Partitioning Algorithms with Variable Importance" (August 2005). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 185.
https://biostats.bepress.com/ucbbiostat/paper185
COinS