[R] Decision Tree and Random Forrest

Michael Eugene
Thu, 14 Apr 2016 01:12:53 -0000

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
> <javascript:_e(%7B%7D,'cvml','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
> <javascript:_e(%7B%7D,'cvml','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
> <javascript:_e(%7B%7D,'cvml','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
> <javascript:_e(%7B%7D,'cvml','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
> <javascript:_e(%7B%7D,'cvml','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