Predicting the strength properties of wood composites using boosted regression trees
Carty, D. M., T. M. Young, R. L. Zaretzki, F. M. Guess, and A. Petutschnigg.  2015.  Forest Products Journal, 65(7/8):365-371.

Abstract:
Predictive boosted regression tree (BRT) models were developed to predict modulus of rupture (MOR) and internal bond (IB) for a US particleboard manufacturer. The temporal process data consisted of 4,307 records and spanned the time frame from March 2009 to June 2010. This study builds on previous published research by developing BRT models across all product types of MOR and IB produced by the particleboard manufacturer. A total of 189 continuous variables from the process line were used as possible predictor variables. BRT model comparisons were made using the root mean squared error for prediction (RMSEP) and the RMSEP relative to the mean of the response variable as a percent (RMSEP%) for the validation data sets. For MOR, RMSEP values ranged from 1.051 to 1.443 MPa, and RMSEP% values ranged from 8.5 to 11.6 percent. For IB, RMSEP values ranged from 0.074 to 0.108 MPa, and RMSEP% values ranged from 12.7 to 18.6 percent. BRT models for MOR and IB predicted better than respective regression tree models without boosting. For MOR, key predictors in the BRT models were related to ‘‘pressing temperature zones,’’ ‘‘thickness of pressing,’’ and ‘‘pressing pressure.’’ For IB, key predictors in the BRT models were related to ‘‘thickness of pressing.’’ The BRT predictive models offer manufacturers an opportunity to improve the understanding of processes and be more predictive in the outcomes of product quality attributes. This may help manufacturers reduce rework and scrap and also improve production efficiencies by avoiding unnecessarily high operating targets.