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
Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings
TL;DR: In this paper, the authors presented a data-driven forecasting model for day-ahead electricity usage of buildings in 15-minute resolution by using variable importance analysis and selected key variables: day type indicator, time-of-day, HVAC set temperature schedule, outdoor air dry-bulb temperature, and outdoor humidity as the most important predictors for electricity consumption.
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Large stocks of peatland carbon and nitrogen are vulnerable to permafrost thaw.
Gustaf Hugelius,Gustaf Hugelius,Julie Loisel,Sarah Chadburn,Robert B. Jackson,Miriam C. Jones,Glen M. MacDonald,Maija E. Marushchak,David Olefeldt,Maara S. Packalen,Matthias Benjamin Siewert,Claire C. Treat,Merritt R. Turetsky,Merritt R. Turetsky,Carolina Voigt,Zicheng Yu,Zicheng Yu +16 more
TL;DR: This study compiles over 7,000 field observations to present a data-driven map of northern peatlands and their carbon and nitrogen stocks, and uses machine-learning techniques with extensive peat core data to create observation-based maps ofNorthern peatland C and N stocks and to assess their response to warming and permafrost thaw.
Proceedings Article
Classification with class imbalance problem: A review
TL;DR: The advancement of machine learning techniques would mostly benefit the big data computing in addressing the class imbalance problem which is inevitably presented in many real world applications especially in medicine and social media.
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Risk assessment in social lending via random forests
TL;DR: Results on data from the popular social lending platform Lending Club indicate the RF-based method outperforms the FICO credit scores as well as LC grades in identification of good borrowers.
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Review on probabilistic forecasting of photovoltaic power production and electricity consumption
TL;DR: In this paper, the authors present a review of the state of the art in the area of probabilistic forecasting of solar power (PSPF) and load forecasting (PLF).
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.