# [R] Decision Tree and Random Forrest

Michael Artz michaeleartz at gmail.com
Thu Apr 14 00:45:11 CEST 2016

```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.

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:

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
>> >>

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