Two-level quantile regression forests for bias correction in range prediction

DOI10.1007/s10994-014-5452-1zbMath1343.62022OpenAlexW2007448114MaRDI QIDQ890300

N. E. Zubov

Publication date: 10 November 2015

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-014-5452-1

zbMATH Keywords

data miningrandom forestsbias correctionhigh dimensional dataquantile regression forests

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

An efficient random forests algorithm for high dimensional data classification

Uses Software

• party
• Boruta
• randomForest
• Quantregforest

Cites Work

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• Bagging predictors
• Multivariate adaptive regression splines
• Robustness of random forests for regression
• 10.1162/153244303322753733
• Bias-corrected random forests in regression
• Random forests
• Using iterated bagging to debias regressions

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