If you just want to be able to use the trained RF model in some future R session for prediction on new data, just use save() to save the RF object, and load() it back in the future.
If you really want to write your own low-level code for prediction, you can take a look at the predictRegTree() function in randomForest/src/regTree.c (the last function in that file). It shows how prediction is done using the data structure from a randomForest object. Andy From: Christian Sturz > > I've tried the getTree() function and printed a decision tree > with print(). > However, it seems to me that it's hard to parse this > representation and > translate it into equivalent if-then-else C constructs. Are > there no other > ways to dump the trees into a more hierarchical form? > > What do you exactly mean with the prediction in the source package? > > Maybe what I wanted to ask goes in the same direction: let's > say I've learned > a random forest model from a learning set. Now I would like > to use it in the > future as classifier to predict new examples. How can this be > done? Can I save > a learned model and than invoke R with new examples and > applied them to > the saved model without again training the random forest from > scratch? If so, > please give me some hints how to do that. > > Regards, > Chris > > -------- Original-Nachricht -------- > > Datum: Thu, 9 Oct 2008 14:38:44 -0400 > > Von: "Liaw, Andy" <[EMAIL PROTECTED]> > > An: "Christian Sturz" <[EMAIL PROTECTED]>, r-help@r-project.org > > Betreff: RE: [R] Dump decision trees of randomForest object > > > See the getTree() function in the package. Also, the source package > > contains C code that does the prediction that you may be > able to work > > from. > > > > Andy > > > > From: Christian Sturz > > > > > > Hi, > > > > > > I'm using the package randomForest to generate a classifier > > > for the exemplary > > > iris data set: > > > > > > data(iris) > > > iris.rf<-randomForest(Species~.,iris) > > > > > > Is it possible to print all decision trees in the > generated forest? > > > If so, can the trees be also written to disk? > > > > > > What I actually need is to translate the decision trees in a > > > random forest > > > into equivalent C++ if-then-else constructs to integrate > them in a C++ > > > project. Have this been done in the past and are there already any > > > implemented approaches/parser for that? > > > > > > Cheers, > > > Chris > > > -- > > > > > > ______________________________________________ > > > R-help@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. > > > > > Notice: This e-mail message, together with any > attachments, contains > > information of Merck & Co., Inc. (One Merck Drive, > Whitehouse Station, > > New Jersey, USA 08889), and/or its affiliates (which may be known > > outside the United States as Merck Frosst, Merck Sharp & Dohme or > > MSD and in Japan, as Banyu - direct contact information for > affiliates is > > available at http://www.merck.com/contact/contacts.html) that may be > > confidential, proprietary copyrighted and/or legally > privileged. It is > > intended solely for the use of the individual or entity > named on this > > message. If you are not the intended recipient, and have > received this > > message in error, please notify us immediately by reply e-mail and > > then delete it from your system. > > -- > Psssst! Schon vom neuen GMX MultiMessenger gehört? Der kann`s > mit allen: http://www.gmx.net/de/go/multimessenger > Notice: This e-mail message, together with any attachme...{{dropped:12}} ______________________________________________ R-help@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.
