Thanks bill that will give the result I would like, however the example I used is not the actual data I'm working with. I have 25 or so columns, each with 1-5 factors and 4 off them are numerical.
On Fri, Apr 15, 2016 at 5:44 PM, William Dunlap <wdun...@tibco.com> wrote: > Since you only have 3 predictors, each categorical with a small number of > categories, you can use expand.grid to make a data.frame containing all > possible combinations and give that the predict method for your model to > get all possible predictions. > > Something like the following untested code. > newdata <- expand.grid( > Humidity = levels(Humidity), #(High, Medium,Low) > Pending_Chores = levels(Pending_Chores), #(Taxes, None, Laundry, > Car Maintenance) > Wind = levels(Wind)) # (High,Low) > newdata$ProbabilityOfPlayingGolf <- predict(fittedModel, > newdata=newdata) > > > Bill Dunlap > TIBCO Software > wdunlap tibco.com > > On Fri, Apr 15, 2016 at 3:09 PM, Michael Artz <michaelea...@gmail.com> > wrote: > >> 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. >> > > [[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.
