[R] Decision Tree and Random Forrest

Sarah Goslee
Thu, 14 Apr 2016 07:26:41 -0000

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