[R] Decision Tree and Random Forrest
Michael Artz
michaeleartz at gmail.com
Sat Apr 16 06:58:20 CEST 2016
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 <wdunlap at 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 <michaeleartz at 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.goslee at 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 <fartzy at 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.goslee at gmail.com>
>> >> Date: 4/13/16 8:04 PM (GMT-06:00)
>> >> To: Michael Artz <michaeleartz at gmail.com>
>> >> Cc: "r-help at r-project.org" <R-help at r-project.org>
>> >> Subject: Re: [R] Decision Tree and Random Forrest
>> >>
>> >>
>> >>
>> >> On Wednesday, April 13, 2016, Michael Artz <michaeleartz at 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.goslee at 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 <michaeleartz at 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.4567 at 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 <
>> michaeleartz at 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.4567 at 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
>> >
>>
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>>
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