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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Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation
TL;DR: Individual conditional expectation plots (ICE) as discussed by the authors can be used to visualize the average partial relationship between the predicted response and one or more features in the context of a supervised learning algorithm.
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A survey of multiple classifier systems as hybrid systems
TL;DR: An up-to-date survey on multiple classifier system (MCS) from the point of view of Hybrid Intelligent Systems is presented, providing a vision of the spectrum of applications that are currently being developed.
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A general-purpose machine learning framework for predicting properties of inorganic materials
TL;DR: This manuscript has created a framework capable of being applied to a broad range of materials data, and demonstrates how this new method can be used to predict diverse properties of crystalline and amorphous materials, such as band gap energy and glass-forming ability.
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Empirical characterization of random forest variable importance measures
Kellie J. Archer,Ryan V. Kimes +1 more
TL;DR: The RF methodology is attractive for use in classification problems when the goals of the study are to produce an accurate classifier and to provide insight regarding the discriminative ability of individual predictor variables.
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Definitions, methods, and applications in interpretable machine learning.
TL;DR: The authors define interpretability in the context of machine learning and introduce the predictive, descriptive, relevant (PDR) framework for discussing interpretations, with three overarching desiderata for evaluation: predictive accuracy, descriptive accuracy, and relevancy, with relevance judged relative to a human audience.
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.