Decision Tree and Random Forrest

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

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