Re: [R] Boosting,bagging and bumping. Questions about R tools and predictions.
Take a look at the randomForest package on CRAN: randomForest: Breiman's random forest for classification and regression Classification and regression based on a forest of trees using random inputs. Version: 3.9-6 Depends: R (= 1.7.0) Author: Fortran original by Leo Breiman and Adele Cutler, R port by Andy Liaw and Matthew Wiener. Maintainer: Andy Liaw [EMAIL PROTECTED] which has a predict function HTH Gav monkeychump wrote: I'm interested in further understanding the differences in using many classification trees to improve classification rates. I'm also interested in finding out what I can do in R and which methods will allow prediction. Can anybody point me to a citation or discussion? Specifically, I want to classify remotely sensed imagery where training data is extracted on class membership by the user. That training data (usually spectral bands and categorical data - e.g., soil type) is classified (using rpart for instance) and then the resulting tree is applied to the entire image. This results in a classified image that can then be checked for accuracy. Classification trees are increasingly used by the remote sensing folks but it seems like finding optimal trees is an active area of research in computational statistics. I've seen great claims made by baggers and boosters (and just what is bumping?) of increasing classification accuracy but aside from TreeNet by Salford Systems I'm not aware of tools that can grow forests of trees that can then be used to make predictions. Can anybody help? Promote security and make money with the Hushmail Affiliate Program: __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help -- %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% Gavin Simpson [T] +44 (0)20 7679 5522 ENSIS Research Fellow [F] +44 (0)20 7679 7565 ENSIS Ltd. ECRC [E] [EMAIL PROTECTED] UCL Department of Geography [W] http://www.ucl.ac.uk/~ucfagls/cv/ 26 Bedford Way[W] http://www.ucl.ac.uk/~ucfagls/ London. WC1H 0AP. %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
RE: [R] Boosting,bagging and bumping. Questions about R tools and predictions.
http://www.boosting.org/publications.html I found some of the papers on
this page useful in understanding the concepts you refer to. I will
leave it to the better informed members of the group to talk about the
packages that relate to this field.
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From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
Sent: Wednesday, 23 July 2003 8:10 AM
To: [EMAIL PROTECTED]
Subject: [R] Boosting,bagging and bumping. Questions about R tools and
predictions.
I'm interested in further understanding the differences in using many
classification trees to improve classification rates. I'm also
interested in finding out what I can do in R and which methods will
allow prediction. Can anybody point me to a citation or discussion?
_
Tom Mulholland
Senior Policy Officer
WA Country Health Service
189 Royal St, East Perth, WA, 6004
Tel: (08) 9222 4062
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