[R] seek non-black box alternative to randomForest

[Bert Gunter]

On Tue, May 30, 2017 at 12:11 PM, Don McKenzie <dmck at u.washington.edu> wrote:
> 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...

... But hopefully will not, as this could quickly devolve into endless
opining that would clog up this list, as you have yourself noted.

-- Bert


>
>
>> 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.
>>
>> --
>>
>>       [[alternative HTML version deleted]]
>>
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>
>
>
> ______________________________________________
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> and provide commented, minimal, self-contained, reproducible code.