Random Forests
Leo Breiman
- Vol. 45, Iss: 1, pp 5-32
TLDR
Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.Abstract:
Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, aaa, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.read more
Citations
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Journal ArticleDOI
Structure and function of the global topsoil microbiome.
Mohammad Bahram,Mohammad Bahram,Mohammad Bahram,Falk Hildebrand,Sofia K. Forslund,Sofia K. Forslund,Jennifer L. Anderson,Nadejda A. Soudzilovskaia,Peter M. Van Bodegom,Johan Bengtsson-Palme,Sten Anslan,Sten Anslan,Luis Pedro Coelho,Helery Harend,Jaime Huerta-Cepas,Marnix H. Medema,Mia R. Maltz,Sunil Mundra,Pål Axel Olsson,Mari Pent,Sergei Põlme,Shinichi Sunagawa,Martin Ryberg,Leho Tedersoo,Peer Bork,Peer Bork +25 more
TL;DR: It is shown that bacterial, but not fungal, genetic diversity is highest in temperate habitats and that microbial gene composition varies more strongly with environmental variables than with geographic distance, and that the relative contributions of these microorganisms to global nutrient cycling varies spatially.
Proceedings Article
Learning To Count Objects in Images
TL;DR: This work focuses on the practically-attractive case when the training images are annotated with dots, and introduces a new loss function, which is well-suited for visual object counting tasks and at the same time can be computed efficiently via a maximum subarray algorithm.
Journal ArticleDOI
Evaluation of consensus methods in predictive species distribution modelling
TL;DR: In this article, the authors tested the predictive accuracies of five consensus methods, namely Weighted Average (WA), Mean(All), Median(All, Median(PCA), and Best, for 28 threatened plant species.
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Measuring ecological niche overlap from occurrence and spatial environmental data
Olivier Broennimann,Matthew C. Fitzpatrick,Peter B. Pearman,Blaise Petitpierre,Loïc Pellissier,Nigel G. Yoccoz,Wilfried Thuiller,Marie-Josée Fortin,Christophe F. Randin,Niklaus E. Zimmermann,Catherine H. Graham,Antoine Guisan +11 more
TL;DR: A statistical framework to describe and compare environmental niches from occurrence and spatial environmental data and shows that niche overlap can be accurately detected with the framework when variables driving the distributions are known.
Journal ArticleDOI
Machine learning methods for solar radiation forecasting: A review
Cyril Voyant,Gilles Notton,Soteris A. Kalogirou,Marie Laure Nivet,Christophe Paoli,Christophe Paoli,Fabrice Motte,Alexis Fouilloy +7 more
TL;DR: An overview of forecasting methods of solar irradiation using machine learning approaches is given and it will be shown that other methods begin to be used in this context of prediction.
References
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Journal ArticleDOI
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Proceedings Article
Experiments with a new boosting algorithm
Yoav Freund,Robert E. Schapire +1 more
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Journal ArticleDOI
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
Eric Bauer,Ron Kohavi +1 more
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