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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
CAMP: Collection of sequences and structures of antimicrobial peptides
Faiza Hanif Waghu,Lijin Gopi,Ram Shankar Barai,Pranay Ramteke,Bilal Nizami,Susan Idicula-Thomas +5 more
TL;DR: The updated Collection of Antimicrobial Peptide (CAMP) database is described, available online at http://www.camp.res.in, and sequence and structure analysis tools have been incorporated to enhance the usefulness of the database.
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Sewage reflects the microbiomes of human populations.
Ryan J. Newton,Sandra L. McLellan,Deborah K. Dila,Joseph H. Vineis,Hilary G. Morrison,A. Murat Eren,Mitchell L. Sogin +6 more
TL;DR: It is demonstrated that sewage represents the fecal microbial community of human populations and captures population-level traits of the human microbiome.
Proceedings ArticleDOI
Opprentice: Towards Practical and Automatic Anomaly Detection Through Machine Learning
TL;DR: The proposed system, Opprentice (Operators' apprentice), allows operators to label data in only tens of minutes, while operators traditionally have to spend more than ten days selecting and tuning detectors, which may still turn out not to work in the end.
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An integrated approach to characterize genetic interaction networks in yeast metabolism
Balázs Szappanos,Károly Kovács,Béla Szamecz,Frantisek Honti,Frantisek Honti,Michael Costanzo,Anastasia Baryshnikova,Gabriel Gelius-Dietrich,Martin J. Lercher,Márk Jelasity,Chad L. Myers,Brenda J. Andrews,Charles Boone,Stephen G. Oliver,Csaba Pál,Balázs Papp,Balázs Papp +16 more
TL;DR: A mechanistic explanation for the link between the degree of genetic interaction, pleiotropy and gene dispensability is provided and the feasibility of automated metabolic model refinement is shown by correcting misannotations in NAD biosynthesis and confirming them by in vivo experiments.
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Day-ahead load forecast using random forest and expert input selection
TL;DR: This paper proposes a short term load predictor, able to forecast the next 24 h of load, constructed following an online learning process using random forest and refined by expert feature selection using a set of if–then rules.
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.