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Visualizing Multi-Variable Prediction Functions by Segmented k-CPG`s
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 Title & Authors
Visualizing Multi-Variable Prediction Functions by Segmented k-CPG`s
Huh, Myung-Hoe;
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 Abstract
Machine learning methods such as support vector machines and random forests yield nonparametric prediction functions of the form y
 Keywords
Visualization of prediction functions;k-Means clustering;variable importance;support vector machine;random forests;environmental data;
 Language
English
 Cited by
1.
Visualizing SVM Classification in Reduced Dimensions,;;

Communications for Statistical Applications and Methods, 2009. vol.16. 5, pp.881-889 crossref(new window)
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Hastie, T., Tibshirani, R. and Friedman, J. (2001). The Elements of Statistical Learning, Springer, New York

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Huh, M. H. and Lee, Y. (2008). Simple graphs for complex prediction functions, Communications of the Korean Statistical Society, 15, 343-351 crossref(new window)

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Strobl, C., Boulesteix, A., Kneib., T., Augustin, T. and Zeileis, A. (2008). Conditioning variable importance for random forests, BMC Bioinformatics, 9, 307 crossref(new window)