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We used the randomForest package for R-language (http://cran.r-project.org/web/packages/randomForest/index.html) [75] to train and test our prediction model.
We used the randomForest package of R [ 31].
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The random forest classification models used in this article were constructed using the randomForest package [54] for R [55] using the approach developed by Breiman [54, 56].
Internally, sdfRRandomForestCreator and sdfRModelPredictor convert the input SD file to tab format and then create models and make predictions using the randomforest package in R [8, 22, 23].
To compare to other feature selection methods, we used the training data to build a Random Forest model [34] using the randomForest package in R (using the default settings of mtry = N/3, ntree = 500, nodesize = 5).
Analysis was conducted in R using the randomForest package (version 4.5-18) by Liaw and Wiener.
Random Forest analyses were performed in R using the "randomForest" package.
RF classification models were implemented using the randomForest package [ 71] in R v2.15.1 (R Foundation for Statistical Computing, Vienna, Austria).
In this study, we constructed randomForest in identifying relevant variables to our outcome variable using the randomForest package in R (2.10.1).
A model was defined on the training set and then assessed on the test set using the randomForest package (Liaw and Wiener, 2002) with ntree set to 1000.
As the method was not available on WEKA, this step was performed using the randomForest package developed by Liaw and Wiener in the statistical software R [ 36, 37].
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