Abstract
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.
Highlights
Every teacher knows that generating succinct explanations means skipping over tedious details
Using an iterative exploration of data mining techniques, we found a particular combination of methods that yielded succinct explanations of how to predict for runaway software projects while
We show how this study found data mining methods that significantly out-perform that prior work
Summary
Every teacher knows that generating succinct explanations means skipping over tedious details. Such explanations can be quickly communicated, but can miss the details needed to apply that knowledge in a real world setting. An analogous situation occurs with data miners. All data miners are performance systems; i.e. they can reach conclusions about a test case. Only some data miners are explanation systems that offer a high-level description of how the learned model functions. The ability to explain how a conclusion was reached is a very powerful tool for helping users to understand and accept the conclusions of a data miner. Sometimes explanatory power must be decreased in order to increase the efficacy of the predictor. Previously Abe, Muzono, Takagi, et al used a Näive
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