Closed-form dual perturb and combine for tree-based models

Keywords

Interpretability, Computer science, Tree (set theory), Variance (accounting), Decision tree

References

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Cited by

• Supervised learning with decision tree-based methods in computational and systems biology.Pierre Geurts, Alexandre Irrthum, Louis Wehenkel — 2009
• Prediction intervals for electric load forecast: Evaluation for different profilesVânia Almeida, João Gama — 2015
• Comments on: A random forest guided tourPierre Geurts, Louis Wehenkel — 2016
• Neural Random ForestsGérard Biau, Erwan Scornet, Johannes Welbl — 2016
• Regularisation of neural networks by enforcing Lipschitz continuityHenry Gouk, Eibe Frank, Bernhard Pfahringer — 2018
• Comprehensive modelling of rotary desiccant wheel with different multiple regression and machine learning methods for balanced flowYunus Emre Güzelel, Umutcan Olmuş, Kamil Neyfel Çerçi — 2021
• Suspended sediment load modeling using advanced hybrid rotation forest based elastic network approachKhabat Khosravi, Ali Golkarian, Assefa M. Melesse — 2022
• Multi-level Machine Learning-Driven Tunnel Squeezing Prediction: Review and New InsightsHadi Fathipour‐Azar — 2022
• Landslide susceptibility mapping of the Ha Long_Van Don Highway using novel ensemble models based on dual perturb and combine for three-based (DPCT)Tuan-Nghia Do, Tran Van Phong, Phan Trọng Trịnh — 2026
• Predicting reference evapotranspiration using the weighted instance handler wrapper algorithmKhabat Khosravi, Aitazaz A. Farooque, Heydar Mirzaei — 2026
• Measures for Combining Prediction Intervals Uncertainty and Reliability in ForecastingVânia Almeida, João Gama — 2016
• An Empirical and Formal Analysis of Decision Trees for RankingEyke Hüllermeier — 2008

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