Ah yes I will have to use the predict function. But the predict function will not get me there really. If I can take the example that I have a model predicting whether or not I will play golf (this is the dependent value), and there are three independent variables Humidity(High, Medium, Low), Pending_Chores(Taxes, None, Laundry, Car Maintenance) and Wind (High, Low). I would like rules like where any record that follows these rules (IF humidity = high AND pending_chores = None AND Wind = High THEN 77% there is probability that play_golf is YES). I was thinking that random forrest would weight the rules somehow on the collection of trees and give a probability. But if that doesnt make sense, then can you just tell me how to get the decsion rules with one tree and I will work from that.
Mike Mike On Wed, Apr 13, 2016 at 4:30 PM, Bert Gunter <bgunter.4...@gmail.com> wrote: > I think you are missing the point of random forests. But if you just > want to predict using the forest, there is a predict() method that you > can use. Other than that, I certainly don't understand what you mean. > Maybe someone else might. > > Cheers, > Bert > > > Bert Gunter > > "The trouble with having an open mind is that people keep coming along > and sticking things into it." > -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) > > > On Wed, Apr 13, 2016 at 2:11 PM, Michael Artz <michaelea...@gmail.com> > wrote: > > Ok is there a way to do it with decision tree? I just need to make the > > decision rules. Perhaps I can pick one of the trees used with Random > > Forrest. I am somewhat familiar already with Random Forrest with > respective > > to bagging and feature sampling and getting the mode from the leaf nodes > and > > it being an ensemble technique of many trees. I am just working from the > > perspective that I need decision rules, and I am working backward form > that, > > and I need to do it in R. > > > > On Wed, Apr 13, 2016 at 4:08 PM, Bert Gunter <bgunter.4...@gmail.com> > wrote: > >> > >> Nope. > >> > >> Random forests are not decision trees -- they are ensembles (forests) > >> of trees. You need to go back and read up on them so you understand > >> how they work. The Hastie/Tibshirani/Friedman "The Elements of > >> Statistical Learning" has a nice explanation, but I'm sure there are > >> lots of good web resources, too. > >> > >> Cheers, > >> Bert > >> > >> > >> Bert Gunter > >> > >> "The trouble with having an open mind is that people keep coming along > >> and sticking things into it." > >> -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) > >> > >> > >> On Wed, Apr 13, 2016 at 1:40 PM, Michael Artz <michaelea...@gmail.com> > >> wrote: > >> > Hi I'm trying to get the top decision rules from a decision tree. > >> > Eventually I will like to do this with R and Random Forrest. There > has > >> > to > >> > be a way to output the decsion rules of each leaf node in an easily > >> > readable way. I am looking at the randomforrest and rpart packages > and I > >> > dont see anything yet. > >> > Mike > >> > > >> > [[alternative HTML version deleted]] > >> > > >> > ______________________________________________ > >> > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > >> > 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. > > > > > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.