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Classification and Regression by randomForest
Andy Liaw,Matthew C. Wiener +1 more
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TLDR
random forests are proposed, which add an additional layer of randomness to bagging and are robust against overfitting, and the randomForest package provides an R interface to the Fortran programs by Breiman and Cutler.Abstract:
Recently there has been a lot of interest in “ensemble learning” — methods that generate many classifiers and aggregate their results. Two well-known methods are boosting (see, e.g., Shapire et al., 1998) and bagging Breiman (1996) of classification trees. In boosting, successive trees give extra weight to points incorrectly predicted by earlier predictors. In the end, a weighted vote is taken for prediction. In bagging, successive trees do not depend on earlier trees — each is independently constructed using a bootstrap sample of the data set. In the end, a simple majority vote is taken for prediction. Breiman (2001) proposed random forests, which add an additional layer of randomness to bagging. In addition to constructing each tree using a different bootstrap sample of the data, random forests change how the classification or regression trees are constructed. In standard trees, each node is split using the best split among all variables. In a random forest, each node is split using the best among a subset of predictors randomly chosen at that node. This somewhat counterintuitive strategy turns out to perform very well compared to many other classifiers, including discriminant analysis, support vector machines and neural networks, and is robust against overfitting (Breiman, 2001). In addition, it is very user-friendly in the sense that it has only two parameters (the number of variables in the random subset at each node and the number of trees in the forest), and is usually not very sensitive to their values. The randomForest package provides an R interface to the Fortran programs by Breiman and Cutler (available at http://www.stat.berkeley.edu/ users/breiman/). This article provides a brief introduction to the usage and features of the R functions.read more
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Journal ArticleDOI
Diversity in gut bacterial community of school-age children in Asia
Jiro Nakayama,Koichi Watanabe,Jiahui Jiang,Kazunori Matsuda,Shiou Huei Chao,Pri Haryono,Orawan La-ongkham,Martinus Agus Sarwoko,I Nengah Sujaya,Liang Zhao,Kang Ting Chen,Yen Po Chen,Hsueh Hui Chiu,Tomoko Hidaka,Ning Xin Huang,Chikako Kiyohara,Takashi Kurakawa,Naoshige Sakamoto,Kenji Sonomoto,Kousuke Tashiro,Hirokazu Tsuji,Ming-Ju Chen,Vichai Leelavatcharamas,Chii Cherng Liao,Sunee Nitisinprasert,Endang Sutriswati Rahayu,Fa Zheng Ren,Ying-Chieh Tsai,Yuan-Kun Lee +28 more
TL;DR: Children living in Japan harbored a less diversified microbiota with high abundance of Bifidobacterium and less number of potentially pathogenic bacteria, which may reflect their living environment and unique diet.
Proceedings ArticleDOI
Variable selection using Mean Decrease Accuracy and Mean Decrease Gini based on Random Forest
Hong Han,Xiaoling Guo,Hua Yu +2 more
TL;DR: A new method is proposed based on Random Forest to select variables using Mean Decrease Accuracy (MDA) and Mean decrease Gini (MDG) and it is proved to perform very fast.
Journal ArticleDOI
Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping
Natan Micheletti,Loris Foresti,Sylvain Robert,Michael N. Leuenberger,Andrea Pedrazzini,Michel Jaboyedoff,Mikhail Kanevski +6 more
TL;DR: The results of this study reveal the strengths of the classification algorithms, but evidence shows the need for relying on more than one method for the identification of relevant variables; the weakness of the adaptive scaling algorithm when used with landslide data; and the lack of additional features which characterize the spatial distribution of deep-seated landslides.
Journal ArticleDOI
Variable importance in regression models
TL;DR: The various variable importance metrics for the linear model, particularly emphasizing variance decomposition metrics, are reviewed, with a focus on linear parametric models.
Journal ArticleDOI
ADHD in girls and boys – gender differences in co-existing symptoms and executive function measures
TL;DR: Self-report scales may increase awareness of internalizing problems particularly salient in females with ADHD, and parent rating scales for the identification of different comorbid symptom expression in boys and girls already diagnosed with ADHD are emphasized.
References
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Modern Applied Statistics With S
TL;DR: The modern applied statistics with s is universally compatible with any devices to read, and is available in the digital library an online access to it is set as public so you can download it instantly.
Proceedings Article
Boosting the margin: A new explanation for the effectiveness of voting methods
TL;DR: In this paper, the authors show that the test error of the generated classifier usually does not increase as its size becomes very large, and often is observed to decrease even after the training error reaches zero.
Journal ArticleDOI
Estimating Generalization Error on Two-Class Datasets Using Out-of-Bag Estimates
TL;DR: For two-class datasets, a method for estimating the generalization error of a bag using out-of-bag estimates is provided and most of the bias is eliminated and accuracy is increased by incorporating a correction based on the distribution of the out- of-bag votes.