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NTIS 바로가기응용통계연구 = The Korean journal of applied statistics, v.34 no.2, 2021년, pp.177 - 190
신승범 (고려대학교 통계학과) , 조형준 (고려대학교 통계학과)
Random forests is a popular method that improves the instability and accuracy of decision trees by ensembles. In contrast to increasing the accuracy, the ease of interpretation is sacrificed; hence, to compensate for this, variable importance is provided. The variable importance indicates which vari...
Archer, K. J. and Kimes, R. V. (2008). Empirical characterization of random forest variable importance measures, Computational Statistics & Data Analysis, 52, 2249-2260.
Biau, G. and Scornet, E. (2016). A random forest guided tour, Text, 25, 197-227.
Breiman, L. (2001). Random forests, Machine Learning, 45, 5-32.
Breiman, L. (2002). Manual on setting up, using, and understanding random forests v3.1. Statistics Department University of California Berkeley, CA, USA.
Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1984). Classification and Regression Trees, Wadsworth, Belmont.
Genuer, R., Poggi, J. M., and Tuleau-Malot, C. (2010). Variable selection using random forests, Pattern Recognition Letters, 31, 2225-2236.
Gregorutti, B., Michel, B., and Saint-Pierre, P. (2017). Correlation and variable importance in random forests, Statistics and Computing, 27, 659-678.
Nicodemus, K. K., Malley, J. D., Strobl, C., and Ziegler, A. (2010). The behaviour of random forest permutation-based variable importance measures under predictor correlation, Bioinformatics, 11, 1-13.
RColorBrewer, S. and Liaw, M. A. (2018). Package 'random Forest', University of California Berkeley, CA, USA.
Rumao, S. (2019). Exploration of Variable Importance and Variable selection techniques in presence of correlated variables. Rochester Institute of Technology.
Strobl, C., Boulesteix, A. L., Kneib, T., Augustin, T., and Zeileis, A. (2008). Conditional variable importance for random forests, Bioinformatics, 9, 307.
Strobl, C., Boulesteix, A. L., Zeileis, A., and Hothorn, T. (2007). Bias in random forest variable importance measures: Illustrations, sources and a solution, Bioinformatics, 8, 25.
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