Practical Large Scale What-if Queries: Case Studies with Software Risk Assessment (2000)
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BibTeX
@MISC{Menzies00practicallarge,
author = {Tim Menzies},
title = {Practical Large Scale What-if Queries: Case Studies with Software Risk Assessment},
year = {2000}
}
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Abstract
When a lack of data inhibits decision making, large scale what-if queries can be conducted over the uncertain parameter ranges. Such what-if queries can generate an overwhelming amount of data. We describe here a general method for understanding that data. Large scale what-if queries can guide Monte Carlo simulations of a model. Machine learning can then be used to summarize the output. The summarization is an ensemble of decision trees. The TARZAN system can poll the ensemble looking for majority conclusions regarding what factors change the classifications of the data. TARZAN can succinctly present the results from very large what-if queries. For example, in one of the studies presented here, we can view on a page the significant features from what-ifs.
