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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deepDR: a network-based deep learning approach to in silico drug repositioning.
Xiangxiang Zeng,Siyi Zhu,Xiangrong Liu,Yadi Zhou,Ruth Nussinov,Feixiong Cheng,Feixiong Cheng,Feixiong Cheng +7 more
TL;DR: A network-based deep-learning approach for in silico drug repurposing by integrating 10 networks, termed deepDR, which learns high-level features of drugs from the heterogeneous networks by a multimodal deep autoencoder and infer candidates for approved drugs for which they were not originally approved.
Proceedings ArticleDOI
Stochastic gradient boosted distributed decision trees
TL;DR: Two different distributed methods that generates exact stochastic GBDT models are presented, the first is a MapReduce implementation and the second utilizes MPI on the Hadoop grid environment.
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High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models.
TL;DR: Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties.
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Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes
Hongying Jiang,Youping Deng,Huann-Sheng Chen,Lin Tao,Qiuying Sha,Jun Chen,Chung-Jui Tsai,Shuanglin Zhang +7 more
TL;DR: This study provided a valuable method of integrating microarray data sets with different origins, and new methods of selecting a minimum number of marker genes to aid in cancer diagnosis.
Journal ArticleDOI
Pedotransfer functions in Earth system science: challenges and perspectives
Kris Van Looy,Johan Bouma,Michael Herbst,John Koestel,Budiman Minasny,Umakant Mishra,Carsten Montzka,Attila Nemes,Yakov Pachepsky,José Padarian,Marcel G. Schaap,Brigitta Tóth,Brigitta Tóth,Anne Verhoef,Jan Vanderborght,Martine van der Ploeg,Lutz Weihermüller,Steffen Zacharias,Yonggen Zhang,Yonggen Zhang,Harry Vereecken +20 more
TL;DR: In this article, a review of the existing PTFs and new generation of PTF developed in the different disciplines of Earth system science is presented, emphasizing that PTF development has to go hand in hand with suitable extrapolation and upscaling techniques such that the PTF models correctly represent the spatial heterogeneity of soils.
References
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Journal ArticleDOI
Bagging predictors
TL;DR: Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy.
Proceedings Article
Experiments with a new boosting algorithm
Yoav Freund,Robert E. Schapire +1 more
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Journal ArticleDOI
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Journal ArticleDOI
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Journal ArticleDOI
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
Eric Bauer,Ron Kohavi +1 more
TL;DR: It is found that Bagging improves when probabilistic estimates in conjunction with no-pruning are used, as well as when the data was backfit, and that Arc-x4 behaves differently than AdaBoost if reweighting is used instead of resampling, indicating a fundamental difference.