[R] explanation why RandomForest don't require a transformations (e.g. logarithmic) of variables

Liaw, Andy andy_liaw at merck.com
Mon Dec 5 19:59:37 CET 2011

Tree based models (such as RF) are invriant to monotonic transformations in the predictor (x) variables, because they only use the ranks of the variables, not their actual values.  More specifically, they look for splits that are at the mid-points of unique values.  Thus the resulting trees are basically identical regardless of how you transform the x variables.

Of course, the only, probably minor, differences is, e.g., mid-points can be different between the original and transformed data.  While this doesn't impact the training data, it can impact the prediction on test data (although difference should be slight).

Transformation of the response variable is quite another thing.  RF needs it just as much as others if the situation calls for it.


> -----Original Message-----
> From: r-help-bounces at r-project.org 
> [mailto:r-help-bounces at r-project.org] On Behalf Of gianni lavaredo
> Sent: Monday, December 05, 2011 1:41 PM
> To: r-help at r-project.org
> Subject: [R] explanation why RandomForest don't require a 
> transformations (e.g. logarithmic) of variables
> Dear Researches,
> sorry for the easy and common question. I am trying to 
> justify the idea of
> RandomForest don't require a transformations (e.g. logarithmic) of
> variables, comparing this non parametrics method with e.g. the linear
> regressions. In leteruature to study my phenomena i need to apply a
> logarithmic trasformation to describe my model, but i found RF don't
> required this approach. Some people could suggest me text or 
> bibliography
> to study?
> thanks in advance
> Gianni
> 	[[alternative HTML version deleted]]
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