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
Changes in plant community composition lag behind climate warming in lowland forests.
Romain Bertrand,Romain Bertrand,Jonathan Lenoir,Christian Piedallu,Christian Piedallu,Gabriela Riofrio-Dillon,Gabriela Riofrio-Dillon,Patrice de Ruffray,Claude Vidal,Jean-Claude Pierrat,Jean-Claude Pierrat,Jean-Claude Gégout,Jean-Claude Gégout +12 more
TL;DR: There was a larger temperature lag (by 3.1 times) between the climate and plant community composition in lowland forests than in highland forests, and the explanation lies in the following properties of lowland, as compared to highland, forests: the higher proportion of species with greater ability for local persistence as the climate warms, the reduced opportunity for short-distance escapes, the greater habitat fragmentation.
Journal ArticleDOI
DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability
TL;DR: DynaMut is presented, a web server implementing two distinct, well established normal mode approaches, which can be used to analyze and visualize protein dynamics by sampling conformations and assess the impact of mutations on protein dynamics and stability resulting from vibrational entropy changes.
Journal ArticleDOI
Exosome Transfer from Stromal to Breast Cancer Cells Regulates Therapy Resistance Pathways
Mirjam C. Boelens,Tony J. Wu,Barzin Y. Nabet,Bihui Xu,Yu Qiu,Taewon Yoon,Diana J. Azzam,Christina Twyman-Saint Victor,Brianne Z. Wiemann,Hemant Ishwaran,Petra ter Brugge,Jos Jonkers,Joyce M. Slingerland,Andy J. Minn +13 more
TL;DR: Primary human and/or mouse BrCa analysis support the role of antiviral/NOTCH3 pathways in NOTCH signaling and stroma-mediated resistance, which is abrogated by combination therapy with gamma secretase inhibitors.
Journal ArticleDOI
Identifying Genetic Determinants Needed to Establish a Human Gut Symbiont in Its Habitat
Andrew L. Goodman,Nathan P. McNulty,Yue Zhao,Douglas D. Leip,Robi D. Mitra,Catherine A. Lozupone,Catherine A. Lozupone,Rob Knight,Jeffrey I. Gordon +8 more
TL;DR: This work used massively parallel sequencing to monitor the relative abundance of tens of thousands of transposon mutants of a saccharolytic human gut bacterium, Bacteroides thetaiotaomicron, as they established themselves in wild-type and immunodeficient gnotobiotic mice, in the presence or absence of other human gut commensals.
Journal ArticleDOI
Hough Forests for Object Detection, Tracking, and Action Recognition
TL;DR: Hough forests can be regarded as task-adapted codebooks of local appearance that allow fast supervised training and fast matching at test time that improve the performance of the generalized Hough transform for object detection on a categorical level and extend to new domains such as object tracking and action recognition.
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
TL;DR: This paper describes experiments carried out to assess how well AdaBoost with and without pseudo-loss, performs on real learning problems and compared boosting to Breiman's "bagging" method when used to aggregate various classifiers.
Journal ArticleDOI
The random subspace method for constructing decision forests
TL;DR: A method to construct a decision tree based classifier is proposed that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity.
Journal ArticleDOI
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization
TL;DR: In this article, the authors compared the effectiveness of randomization, bagging, and boosting for improving the performance of the decision-tree algorithm C4.5 and found that in situations with little or no classification noise, randomization is competitive with bagging but not as accurate as boosting.
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.