Re: [R] re gression trees: mean square vs. absolute errors

From: David Katz <david_at_davidkatzconsulting.com>
Date: Tue, 25 Mar 2008 12:35:29 -0700 (PDT)

You need to think through the application of your model. Is it more important to get more cases classified correctly, or to avoid "bigger" errors versus a probability prediction? You should optimize your choice of a loss function so that it is appropriate to the way in which the model will be used.

lubaroz wrote:
>
> Hi,
> I am working with CART regression now to predict a probability; the
> response is binary. Could anyone tell me in which cases it is better to
> use mean square error for splitting nodes and when mean absolute error
> should be preferred.
> I am now using the default (MSE) version and I can see that the obtained
> optimal tree is very different from the tree with the least mean absolute
> error.
>
> Thanks in advance,
> Luba
>

-- 
View this message in context: http://www.nabble.com/regression-trees%3A-mean-square-vs.-absolute-errors-tp16274094p16286639.html
Sent from the R help mailing list archive at Nabble.com.

______________________________________________
R-help_at_r-project.org mailing list
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.
Received on Tue 25 Mar 2008 - 20:22:12 GMT

Archive maintained by Robert King, hosted by the discipline of statistics at the University of Newcastle, Australia.
Archive generated by hypermail 2.2.0, at Tue 25 Mar 2008 - 20:30:25 GMT.

Mailing list information is available at https://stat.ethz.ch/mailman/listinfo/r-help. Please read the posting guide before posting to the list.

list of date sections of archive