Classification and Regression by randomForest
01 Jan 2007-
TL;DR: 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.
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TL;DR: The need to consider the microbiome when evaluating human development, nutritional needs, physiological variations and the impact of westernization is underscored, as distinctive features of the functional maturation of the gut microbiome are evident in early infancy as well as adulthood.
Abstract: Gut microbial communities represent one source of human genetic and metabolic diversity. To examine how gut microbiomes differ among human populations, here we characterize bacterial species in fecal samples from 531 individuals, plus the gene content of 110 of them. The cohort encompassed healthy children and adults from the Amazonas of Venezuela, rural Malawi and US metropolitan areas and included mono- and dizygotic twins. Shared features of the functional maturation of the gut microbiome were identified during the first three years of life in all three populations, including age-associated changes in the genes involved in vitamin biosynthesis and metabolism. Pronounced differences in bacterial assemblages and functional gene repertoires were noted between US residents and those in the other two countries. These distinctive features are evident in early infancy as well as adulthood. Our findings underscore the need to consider the microbiome when evaluating human development, nutritional needs, physiological variations and the impact of westernization.
6,047 citations
01 Jan 2016
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.
Abstract: Thank you very much for downloading modern applied statistics with s. As you may know, people have search hundreds times for their favorite readings like this modern applied statistics with s, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their laptop. modern applied statistics with s is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the modern applied statistics with s is universally compatible with any devices to read.
5,249 citations
TL;DR: The caret package, short for classification and regression training, contains numerous tools for developing predictive models using the rich set of models available in R to simplify model training and tuning across a wide variety of modeling techniques.
Abstract: The caret package, short for classification and regression training, contains numerous tools for developing predictive models using the rich set of models available in R. The package focuses on simplifying model training and tuning across a wide variety of modeling techniques. It also includes methods for pre-processing training data, calculating variable importance, and model visualizations. An example from computational chemistry is used to illustrate the functionality on a real data set and to benchmark the benefits of parallel processing with several types of models.
5,144 citations
Cites methods from "Classification and Regression by ra..."
...Random forest from Liaw and Wiener (2002): “For each tree, the prediction accuracy on the out-of-bag portion of the data is recorded....
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... Random forest from Liaw and Wiener (2002): “For each tree, the prediction accuracy on the out-of-bag portion of the data is recorded....
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...…and Ripley 1999), nws 1.7.1.0 (Scientific Computing Associates, Inc. 2007), pamr 1.31 (Hastie et al. 2003), party 0.9-96 (Hothorn et al. 2006), pls 2.1-0 (Mevik and Wehrens 2007), randomForest 4.5-25 (Liaw and Wiener 2002), rpart 3.1-39 (Therneau and Atkinson 1997) and SDDA 1.0-3 (Stone 2008)....
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...5-25 (Liaw and Wiener 2002), rpart 3....
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TL;DR: High classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods are observed.
Abstract: Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature.
3,368 citations
TL;DR: The Boruta package provides a convenient interface to the Boruta algorithm, implementing a novel feature selection algorithm for finding emph{all relevant variables}.
Abstract: This article describes a R package Boruta, implementing a novel feature selection algorithm for finding emph{all relevant variables}. The algorithm is designed as a wrapper around a Random Forest classification algorithm. It iteratively removes the features which are proved by a statistical test to be less relevant than random probes. The Boruta package provides a convenient interface to the algorithm. The short description of the algorithm and examples of its application are presented.
2,832 citations
Cites methods from "Classification and Regression by ra..."
...Boruta algorithm is a wrapper built around the random forest classification algorithm implemented in the R package randomForest (Liaw and Wiener 2002)....
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References
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01 Jan 2016
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.
Abstract: Thank you very much for downloading modern applied statistics with s. As you may know, people have search hundreds times for their favorite readings like this modern applied statistics with s, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their laptop. modern applied statistics with s is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the modern applied statistics with s is universally compatible with any devices to read.
5,249 citations
"Classification and Regression by ra..." refers background or methods in this paper
...We use the crabs data in MASS4 to demonstrate the unsupervised learning mode of randomForest....
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...• The number of trees necessary for good performance grows with the number of predictors....
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...We scaled the data as suggested on pages 308–309 of MASS4 (also found in lines 28–29 and 63–68 in ‘$R HOME/library/MASS/scripts/ch11....
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...The Forensic Glass data set was used in Chapter 12 of MASS4 (Venables and Ripley, 2002) to illustrate various classification algorithms....
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Proceedings Article•
AT&T1
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.
Abstract: One of the surprising recurring phenomena observed in experiments with boosting is 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. In this paper, we show that this phenomenon is related to the distribution of margins of the training examples with respect to the generated voting classification rule, where the margin of an example is simply the difference between the number of correct votes and the maximum number of votes received by any incorrect label. We show that techniques used in the analysis of Vapnik's support vector classifiers and of neural networks with small weights can be applied to voting methods to relate the margin distribution to the test error. We also show theoretically and experimentally that boosting is especially effective at increasing the margins of the training examples. Finally, we compare our explanation to those based on the bias-variance
2,423 citations
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.
Abstract: For two-class datasets, we provide a method for estimating the generalization error of a bag using out-of-bag estimates. In bagging, each predictor (single hypothesis) is learned from a bootstrap sample of the training exampless the output of a bag (a set of predictors) on an example is determined by voting. The out-of-bag estimate is based on recording the votes of each predictor on those training examples omitted from its bootstrap sample. Because no additional predictors are generated, the out-of-bag estimate requires considerably less time than 10-fold cross-validation. We address the question of how to use the out-of-bag estimate to estimate generalization error on two-class datasets. Our experiments on several datasets show that the out-of-bag estimate and 10-fold cross-validation have similar performance, but are both biased. We can eliminate most of the bias in the out-of-bag estimate and increase accuracy by incorporating a correction based on the distribution of the out-of-bag votes.
155 citations
"Classification and Regression by ra..." refers background in this paper
...We also thank the reviewer for very helpful comments, and pointing out the reference Bylander (2002)....
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...Our experience has been that the OOB estimate of error rate is quite accurate, given that enough trees have been grown (otherwise the OOB estimate can bias upward; see Bylander (2002))....
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