Open Access
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
Citations
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Mapping the Mouse Cell Atlas by Microwell-Seq
Xiaoping Han,Renying Wang,Yincong Zhou,Lijiang Fei,Huiyu Sun,Shujing Lai,Assieh Saadatpour,Ziming Zhou,Haide Chen,Fang Ye,Daosheng Huang,Yang Xu,Wentao Huang,Mengmeng Jiang,Xinyi Jiang,Jie Mao,Yao Chen,Chenyu Lu,Jin Xie,Qun Fang,Yibin Wang,Rui Yue,Tiefeng Li,He Huang,Stuart H. Orkin,Guo-Cheng Yuan,Ming Chen,Guoji Guo +27 more
TL;DR: This study developed Microwell-seq, a high-throughput and low-cost scRNA-seq platform using simple, inexpensive devices, and built a web-based "single-cell MCA analysis" pipeline that accurately defines cell types based on single-cell digital expression.
Journal ArticleDOI
Shared and distinct transcriptomic cell types across neocortical areas
Bosiljka Tasic,Zizhen Yao,Lucas T. Graybuck,Kimberly A. Smith,Thuc Nghi Nguyen,Darren Bertagnolli,Jeff Goldy,Emma Garren,Michael N. Economo,Sarada Viswanathan,Osnat Penn,Trygve E. Bakken,Vilas Menon,Vilas Menon,Jeremy A. Miller,Olivia Fong,Karla E. Hirokawa,Kanan Lathia,Christine Rimorin,Michael Tieu,Rachael Larsen,Tamara Casper,Eliza Barkan,Matthew Kroll,Sheana Parry,Nadiya V. Shapovalova,Daniel Hirschstein,Julie Pendergraft,Heather A. Sullivan,Tae Kyung Kim,Aaron Szafer,Nick Dee,Peter A. Groblewski,Ian R. Wickersham,Ali Cetin,Julie A. Harris,Boaz P. Levi,Susan M. Sunkin,Linda Madisen,Tanya L. Daigle,Loren L. Looger,Amy Bernard,John W. Phillips,Ed S. Lein,Michael Hawrylycz,Karel Svoboda,Allan R. Jones,Christof Koch,Hongkui Zeng +48 more
TL;DR: This study establishes a combined transcriptomic and projectional taxonomy of cortical cell types from functionally distinct areas of the adult mouse cortex and identifies 133 transcriptomic types of glutamatergic neurons to their long-range projection specificity.
Journal ArticleDOI
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
Stefan Wager,Susan Athey +1 more
TL;DR: This paper developed a non-parametric causal forest for estimating heterogeneous treatment effects that extends Breiman's widely used random forest algorithm, and showed that causal forests are pointwise consistent for the true treatment effect and have an asymptotically Gaussian and centered sampling distribution.
Journal ArticleDOI
Leadership, social capital and incentives promote successful fisheries
TL;DR: Examining 130 co-managed fisheries in a wide range of countries with different degrees of development, ecosystems, fishing sectors and type of resources demonstrates the critical importance of prominent community leaders and robust social capital for successfully managing aquatic resources and securing the livelihoods of communities depending on them.
Journal ArticleDOI
Microbial diversity drives multifunctionality in terrestrial ecosystems
Manuel Delgado-Baquerizo,Fernando T. Maestre,Peter B. Reich,Peter B. Reich,Thomas C. Jeffries,Juan José Gaitán,Daniel Encinar,Miguel Berdugo,Colin Campbell,Brajesh K. Singh +9 more
TL;DR: The findings provide empirical evidence that any loss in microbial diversity will likely reduce multifunctionality, negatively impacting the provision of services such as climate regulation, soil fertility and food and fibre production by terrestrial ecosystems.
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.