Extremely randomized trees

DOI10.1007/s10994-006-6226-1zbMath1470.68111OpenAlexW2056132907WikidataQ56221779 ScholiaQ56221779MaRDI QIDQ5898262

Pierre Geurts, Louis Wehenkel, Damien Ernst

Publication date: 22 November 2006

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

Full work available at URL: https://doi.org/10.1007/s10994-006-6226-1

zbMATH Keywords

supervised learningdecision treesregression treesensemble methodsbias/variance tradeoffcut-point randomizationkernel-based models

Mathematics Subject Classification ID

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

Related Items (14)

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Uses Software

• UCI-ml
• AdaBoost.MH
• ElemStatLearn
• C4.5

Cites Work

• Bagging predictors
• Multivariate adaptive regression splines
• Stochastic discrimination
• Variance and bias for general loss functions
• Inference for the generalization error
• Randomizing outputs to increase prediction accuracy
• Arcing classifiers. (With discussion)
• 10.1162/153244301753683717
• Random Forests and Adaptive Nearest Neighbors
• Extremely randomized trees
• The elements of statistical learning. Data mining, inference, and prediction
• Random forests
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