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Thread: Seek non-black box alternative to random forest

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    Seek non-black box alternative to random forest




    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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    Re: Seek non-black box alternative to random forest


    I thought the random assignment is what made the trees pseudo independent and not a limitation. I am not overly versed in this area. Did the editor reject the paper without consideration of a revision, so it was a straight rejection?


    What info did you provide within the manuscript. I have desired to do the same thing you did, but I thought I would provide the Variable Importance figure. Did you do something like that or were you able to come up with some type of effect size measure with precision estimate?


    P.S., once again I am not seeing the problem with random assignment, that is traditionally what is good about the approach. Also, if you run enough trees, what is there real issue?


    I have in lieu used regularization models with cross-validation, but if you used that you would need to use CV also on your traditional model and show the model with shrinkage terms was possibly better.
    Stop cowardice, ban guns!

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