[R] Decision Tree and Random Forrest

William Dunlap
Fri, 15 Apr 2016 22:44:38 -0000

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
> >
>
>         [[alternative HTML version deleted]]
>
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