Measuring the Algorithmic Convergence of Randomized Ensembles: The Regression Setting

DOI10.1137/20M1343300zbMath1490.62161arXiv1908.01251OpenAlexW3092422740MaRDI QIDQ5037548

Suofei Wu, Miles E. Lopes, Thomas C. M. Lee

Publication date: 1 March 2022

Published in: SIAM Journal on Mathematics of Data Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1908.01251

zbMATH Keywords

bootstraprandomized algorithmsrandom forestsbagging

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Nonparametric statistical resampling methods (62G09) Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• UCI-ml
• ggplot2
• Scikit
• GeneSrF
• randomForest
• TensorFlow
• Comet
• GitHub
• VSURF
• BartPy

Cites Work

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• Random-projection Ensemble Classification
• Random Forests and Adaptive Nearest Neighbors
• The elements of statistical learning. Data mining, inference, and prediction
• Random forests

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