So. Given that the second and third panels of the first figure in the first link I gave show a decision tree with decision rules at each split and the number of samples at each direction, what _exactly_ is your problem?
On Wednesday, April 13, 2016, Michael Eugene <far...@hotmail.com> wrote: > I still need the output to match my requiremnt in my original post. With > decision rules "clusters" and probability attached to them. The examples > are sort of similar. You just provided links to general info about trees. > > > > Sent from my Verizon, Samsung Galaxy smartphone > > > -------- Original message -------- > From: Sarah Goslee <sarah.gos...@gmail.com > <javascript:_e(%7B%7D,'cvml','sarah.gos...@gmail.com');>> > Date: 4/13/16 8:04 PM (GMT-06:00) > To: Michael Artz <michaelea...@gmail.com > <javascript:_e(%7B%7D,'cvml','michaelea...@gmail.com');>> > Cc: "r-help@r-project.org > <javascript:_e(%7B%7D,'cvml','r-help@r-project.org');>" < > R-help@r-project.org > <javascript:_e(%7B%7D,'cvml','R-help@r-project.org');>> > Subject: Re: [R] Decision Tree and Random Forrest > > > > On Wednesday, April 13, 2016, Michael Artz <michaelea...@gmail.com > <javascript:_e(%7B%7D,'cvml','michaelea...@gmail.com');>> wrote: > > 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. > > Then what specifically is your problem? Both of the tutorials I provided > show worked examples, as does even the help for rpart. If none of those, or > your extensive reading, work for your project you will have to be a lot > more specific about why not. > > Sarah > > > > 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 > >> >> > > > > -- > Sarah Goslee > http://www.stringpage.com > http://www.sarahgoslee.com > http://www.functionaldiversity.org > -- Sarah Goslee http://www.stringpage.com http://www.sarahgoslee.com http://www.functionaldiversity.org [[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.