Visualizable and interpretable regression models with good prediction power
Hyunjoong Kim, Wei‐Yin Loh, Yu‐Shan Shih, Probal Chaudhuri · IIE Transactions · 2007
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.