Robustness of random forests for regression
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DOI: 10.1080/10485252.2012.715161
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References listed on IDEAS
- Lessmann, Stefan & Sung, Ming-Chien & Johnson, Johnnie E.V., 2010. "Alternative methods of predicting competitive events: An application in horserace betting markets," International Journal of Forecasting, Elsevier, vol. 26(3), pages 518-536, July.
- Peters, Jan & Baets, Bernard De & Verhoest, Niko E.C. & Samson, Roeland & Degroeve, Sven & Becker, Piet De & Huybrechts, Willy, 2007. "Random forests as a tool for ecohydrological distribution modelling," Ecological Modelling, Elsevier, vol. 207(2), pages 304-318.
- Rokach, Lior, 2009. "Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4046-4072, October.
- Biau, Gérard & Devroye, Luc, 2010. "On the layered nearest neighbour estimate, the bagged nearest neighbour estimate and the random forest method in regression and classification," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2499-2518, November.
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- Ju, Xiaomeng & Salibián-Barrera, Matías, 2021. "Robust boosting for regression problems," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
- Meng Zhang & Jiatong Ling & Buyun Tang & Shaohua Dong & Laibin Zhang, 2022. "A Data-Driven Based Method for Pipeline Additional Stress Prediction Subject to Landslide Geohazards," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
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