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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High-throughput assays for promiscuous inhibitors
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The effects of climate extremes on global agricultural yields
Elisabeth Vogel,Markus G. Donat,Markus G. Donat,Lisa V. Alexander,Malte Meinshausen,Malte Meinshausen,Deepak K. Ray,David J. Karoly,Nicolai Meinshausen,Katja Frieler +9 more
TL;DR: This paper analyzed the impacts of climate extremes on yield anomalies of maize, soybeans, rice and spring wheat at the global scale using sub-national yield data and applying a machine-learning algorithm.
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Predicting customer retention and profitability by using random forests and regression forests techniques
Bart Larivière,Dirk Van den Poel +1 more
TL;DR: The research findings demonstrate that both random forests techniques provide better fit for the estimation and validation sample compared to ordinary linear regression and logistic regression models, and suggest that past customer behavior is more important to generate repeat purchasing and favorable profitability evolutions.
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Comparison of imputation methods for missing laboratory data in medicine
Akbar K. Waljee,Ashin Mukherjee,Amit G. Singal,Yiwei Zhang,Jeffrey S. Warren,Ulysses J. Balis,Jorge A. Marrero,Ji Zhu,Peter D.R. Higgins +8 more
TL;DR: MissForest is a highly accurate method of imputations for missing laboratory data and outperforms other common imputation techniques in terms of imputation error and maintenance of predictive ability with imputed values in two clinical predicative models.
Book
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TL;DR: Data Mining with R: Learning with Case Studies as mentioned in this paper uses practical examples to illustrate the power of R and data mining, assuming no prior knowledge of R or data mining/statistical techniques.
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.