Tjats great that you are familiar and thanks for responding. Have you ever done what I am referring to? I have alteady spent time going through links and tutorials about decision trees and random forrests and have even used them both before.
Mike On Apr 13, 2016 5:32 PM, "Sarah Goslee" <sarah.gos...@gmail.com> wrote: It sounds like you want classification or regression trees. rpart does exactly what you describe. Here's an overview: http://www.statmethods.net/advstats/cart.html But there are a lot of other ways to do the same thing in R, for instance: http://www.r-bloggers.com/a-brief-tour-of-the-trees-and-forests/ You can get the same kind of information from random forests, but it's less straightforward. If you want a clear set of rules as in your golf example, then you need rpart or similar. Sarah On Wed, Apr 13, 2016 at 6:02 PM, Michael Artz <michaelea...@gmail.com> wrote: > 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 >> >> [[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.