Decision Tree and Random Forrest

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
<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