Application of classification trees in the sensitivity analysis of probabilistic model results

The complexity of some integrated-system models necessitates using a probabilistic approach to quantify uncertainty in model projections. In this work, we demonstrate how classification trees can be used to perform sensitivity analyses on probabilistic results. The classification tree technique is applied to results from the probabilistic total system performance assessment model used in the Yucca Mountain project. The technique proves effective in delineating the variables that most influence low and high outcomes.