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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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Identifying the influential features on the regional energy use intensity of residential buildings based on Random Forests
Jun Ma,Jack Chin Pang Cheng +1 more
TL;DR: In this article, the influence of 171 possibly related features on the regional energy use intensity (EUI) of residential buildings using a non-linear regression algorithm, namely Random Forests (RF).
Journal ArticleDOI
Microbial indicators of environmental perturbations in coral reef ecosystems
Bettina Glasl,Bettina Glasl,Bettina Glasl,David G. Bourne,David G. Bourne,David G. Bourne,Pedro R. Frade,Torsten Thomas,Britta Schaffelke,Nicole S. Webster,Nicole S. Webster,Nicole S. Webster +11 more
TL;DR: It is shown that seawater microbiome has the greatest diagnostic value to infer shifts in the surrounding reef environment, and seawater microbial community data provide an accurate prediction of temperature and eutrophication state (i.e. chlorophyll concentration and turbidity).
Journal ArticleDOI
Ensemble Boosting and Bagging Based Machine Learning Models for Groundwater Potential Prediction
Amirhosein Mosavi,Farzaneh Sajedi Hosseini,Bahram Choubin,Massoud Goodarzi,Adrienn Dineva,Elham Rafiei Sardooi +5 more
TL;DR: Groundwater potential maps predicted in this study can help water resources managers and policymakers in the fields of watershed and aquifer management to preserve an optimal exploit from this important freshwater.
Journal ArticleDOI
LMTRDA: Using logistic model tree to predict MiRNA-disease associations by fusing multi-source information of sequences and similarities.
TL;DR: A new model of Logistic Model Tree for predicting miRNA-Disease Association (LMTRDA) is proposed by fusing multi-source information including miRNA sequences, miRNA functional similarity, disease semantic similarity, and known mi RNA-disease associations by introducing miRNA sequence information and extract its features using natural language processing technique for the first time in the miRNA and disease prediction model.
Classification of multispectral images using Random Forest algorithm
Özlem Akar,Oguz Gungor +1 more
TL;DR: Rastgele Orman algoritmasi kullanilarak cok bantli goruntulerin siniflandirma dogrulugu verdigini gostermektedir as discussed by the authors.
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.