I’m interested in the subject. If you send the question to another platform, please share the link here to follow up. Also, I wish to see the manuscript and rejected parts and detailed reasons. Most of the time, scientists want to reveal/discuss underlying physical process in an event and it’s not enough to show that method A is better than method B. Perhaps, discussions and why the randomforest is better than multiple linear regression is not enough for him. This also may mean black box.
> On 30 May 2017, at 22:27, Simmering, Jacob E <jacob-simmer...@uiowa.edu> > wrote: > > Barry, > > This is mostly a mailing list about R - you have have more luck with > statistical questions on www.stat.stackexchange.com. > > That said - the editor is wrong. The limitations of trees that random forests > “solves” is overfitting. The mechanism by which a random forest classifier is > built is not a black box - some number of features and some number of rows > are selected to produce a split. The reasons why this approach avoids the > issues associated with trees is also clear. These are theory based claims. > The random selection is critical to the function of the process. I’d suggest > resubmitting the paper to a different journal instead of trying to find some > way to fit a random forest without the random part. > > >> On May 30, 2017, at 1:54 PM, 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. ______________________________________________ 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.
