[R] Decision Tree and Random Forrest

[Michael Artz]

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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