[R] Decision Tree and Random Forrest
Sarah Goslee
sarah.goslee at gmail.com
Thu Apr 14 09:26:41 CEST 2016
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
> <javascript:_e(%7B%7D,'cvml','sarah.goslee at gmail.com');>>
> Date: 4/13/16 8:04 PM (GMT-06:00)
> To: Michael Artz <michaeleartz at gmail.com
> <javascript:_e(%7B%7D,'cvml','michaeleartz at gmail.com');>>
> Cc: "r-help at r-project.org
> <javascript:_e(%7B%7D,'cvml','r-help at r-project.org');>" <
> R-help at r-project.org
> <javascript:_e(%7B%7D,'cvml','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
> <javascript:_e(%7B%7D,'cvml','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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