Classification and Regression by randomForest

by: Andy Liaw, Matthew Wiener

R News, Vol. 2, No. 3. (December 2002), pp. 18-22.

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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.

@article{Liaw2002_randomforest, 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.}, author = {Liaw, Andy and Wiener, Matthew }, citeulike-article-id = {785144}, journal = {R News}, keywords = {ensemble, machine-learning, randomforest}, month = {December}, number = {3}, pages = {18--22}, posted-at = {2006-08-03 23:30:29}, priority = {2}, title = {Classification and Regression by randomForest}, url = {http://cran.r-project.org/doc/Rnews/Rnews\_2002-3.pdf}, volume = {2}, year = {2002} }

RIS record

TY - JOUR ID - Liaw2002_randomforest L3 - citeulike-article-id:785144 TI - Classification and Regression by randomForest JF - R News VL - 2 IS - 3 SP - 18 EP - 22 N2 - 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. KW - ensemble KW - machine-learning KW - randomforest AU - Liaw, Andy AU - Wiener, Matthew PY - 2002/December// UR - http://cran.r-project.org/doc/Rnews/Rnews_2002-3.pdf ER -

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