[R] seek non-black box alternative to randomForest

[Don McKenzie]

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.king at 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.
> 
> --
> 
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