Visualizable and interpretable regression models with good prediction power

Kim, Hyunjoong ; Loh, Wei-Yin ; Shih, Yu-Shan ; Chaudhuri, Probal (2007) Visualizable and interpretable regression models with good prediction power IIE Transactions, 39 (6). pp. 565-579. ISSN 0740-817X

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Many methods can fit models with a higher prediction accuracy, on average, than the least squares linear regression technique. But the models, including linear regression, are typically impossible to interpret or visualize. We describe a tree-structured method that fits a simple but nontrivial model to each partition of the variable space. This ensures that each piece of the fitted regression function can be visualized with a graph or a contour plot. For maximum interpretability, our models are constructed with negligible variable selection bias and the tree structures are much more compact than piecewise-constant regression trees. We demonstrate, by means of a large empirical study involving 27 methods, that the average prediction accuracy of our models is almost as high as that of the most accurate "black-box" methods from the statistics and machine learning literature.

Item Type:Article
Source:Copyright of this article belongs to Taylor and Francis Group.
Keywords:Machine Learning; Piecewise Linear; Regression Tree; Selection Bias
ID Code:74633
Deposited On:17 Dec 2011 10:37
Last Modified:17 Dec 2011 10:37

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