I need the output to have groups and the probability any given record in that group then has of being in the response class. Just like my email in the beginning i need the output that looks like if A and if B and if C then %77 it will be D. The examples you provided are just simply not similar. They are different and would take interpretation to get what i need. On Apr 14, 2016 1:26 AM, "Sarah Goslee" <sarah.gos...@gmail.com> wrote:
> 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> >> Date: 4/13/16 8:04 PM (GMT-06:00) >> To: Michael Artz <michaelea...@gmail.com> >> Cc: "r-help@r-project.org" <R-help@r-project.org> >> Subject: Re: [R] Decision Tree and Random Forrest >> >> >> >> On Wednesday, April 13, 2016, Michael Artz <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.
