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<div> </div><div> </div><!-- originalMessage --><div>-------- Original message --------</div><div>From: Sarah Goslee <sarah.goslee at gmail.com> </div><div>Date: 4/13/16 8:04 PM (GMT-06:00) </div><div>To: Michael Artz <michaeleartz at gmail.com> </div><div>Cc: "r-help at r-project.org" <R-help at r-project.org> </div><div>Subject: Re: [R] Decision Tree and Random Forrest </div><div> </div>
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 http://www.stringpage.com http://www.sarahgoslee.com http://www.functionaldiversity.org