Simple Graphs for Complex Prediction Functions

Title & Authors
Simple Graphs for Complex Prediction Functions
Huh, Myung-Hoe; Lee, Yong-Goo;

Abstract
By supervised learning with p predictors, we frequently obtain a prediction function of the form $\small{y\;=\;f(x_1,...,x_p)}$. When $\small{p\;{\geq}\;3}$, it is not easy to understand the inner structure of f, except for the case the function is formulated as additive. In this study, we propose to use p simple graphs for visual understanding of complex prediction functions produced by several supervised learning engines such as LOESS, neural networks, support vector machines and random forests.
Keywords
Visualization;prediction function;LOESS;neural network model;support vector machine;random forest;
Language
English
Cited by
1.
Visualizing Multi-Variable Prediction Functions by Segmented k-CPG's,;

Communications for Statistical Applications and Methods, 2009. vol.16. 1, pp.185-193
2.
Visualizing SVM Classification in Reduced Dimensions,;;

Communications for Statistical Applications and Methods, 2009. vol.16. 5, pp.881-889
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