Visualizing Multi-Variable Prediction Functions by Segmented k-CPG's Huh, Myung-Hoe;
Machine learning methods such as support vector machines and random forests yield nonparametric prediction functions of the form y = . As a sequel to the previous article (Huh and Lee, 2008) for visualizing nonparametric functions, I propose more sensible graphs for visualizing y = herein which has two clear advantages over the previous simple graphs. New graphs will show a small number of prototype curves of , revealing statistically plausible portion over the interval of which changes with (). To complement the visual display, matching importance measures for each of p predictor variables are produced. The proposed graphs and importance measures are validated in simulated settings and demonstrated for an environmental study.
Visualization of prediction functions;k-Means clustering;variable importance;support vector machine;random forests;environmental data;