Though off-topic for this list, your question (complaint?) comes up a lot in discussions of analytical methods, and has generated hundreds of papers (Google is your friend here). You can start with
https://www.quora.com/What-are-the-pros-and-cons-of-GLM-vs-Random-forest-vs-SVM <https://www.quora.com/What-are-the-pros-and-cons-of-GLM-vs-Random-forest-vs-SVM> for some of the controversies. It looks to me as if your editor stated (poorly) the problem that some models that are good at pattern-matching (RF) are less useful for predicting new observations. Others n the list who are more erudite than I may choose to comment, amplify, or refute... > On May 30, 2017, at 11:54 AM, Barry King <barry.k...@qlx.com> wrote: > > I've recently had a research manuscript rejected by an editor. The > manuscript showed > that for a real life data set, random forest outperformed multiple linear > regression > with respect to predicting the target variable. The editor's objection was > that > random forest is a black box where the random assignment of features to > trees was > intractable. I need to find an alternative method to random forest that > does not > suffer from the black box label. Any suggestions? Would caret::treebag be > free of > random assignment of features? Your assistance is appreciated. > > -- > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
