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Explanation vs Performance in Data Mining: A Case Study with Predicting Runaway Projects

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DOI: 10.4236/jsea.2009.24030    5,367 Downloads   9,647 Views   Citations


Often, the explanatory power of a learned model must be traded off against model performance. In the case of predict-ing runaway software projects, we show that the twin goals of high performance and good explanatory power are achievable after applying a variety of data mining techniques (discrimination, feature subset selection, rule covering algorithms). This result is a new high water mark in predicting runaway projects. Measured in terms of precision, this new model is as good as can be expected for our data. Other methods might out-perform our result (e.g. by generating a smaller, more explainable model) but no other method could out-perform the precision of our learned model.

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The authors declare no conflicts of interest.

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T. MENZIES, O. MIZUNO, Y. TAKAGI and T. KIKUNO, "Explanation vs Performance in Data Mining: A Case Study with Predicting Runaway Projects," Journal of Software Engineering and Applications, Vol. 2 No. 4, 2009, pp. 221-236. doi: 10.4236/jsea.2009.24030.


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