Re: [R] Decision Tree and Random Forrest

>> >> >> >> >> >> >> >> Sent from my Verizon, Samsung Galaxy smartphone >> >> >> >> >> >> Original message >> >> From: Sarah Goslee <sarah.gos...@gmail.com> >> >> Date: 4/1

Re: [R] Decision Tree and Random Forrest

ples > >> are sort of similar. You just provided links to general info about > trees. > >> > >> > >> > >> Sent from my Verizon, Samsung Galaxy smartphone > >> > >> > >> Original message ---- > >> From

Re: [R] Decision Tree and Random Forrest

essage >> From: Sarah Goslee <sarah.gos...@gmail.com> >> Date: 4/13/16 8:04 PM (GMT-06:00) >> To: Michael Artz <michaelea...@gmail.com> >> Cc: "r-help@r-project.org" <R-help@r-project.org> >> Subject: Re: [R] Decision Tree and Random Forre

Re: [R] Decision Tree and Random Forrest

cript:_e(%7B%7D,'cvml','michaelea...@gmail.com');>> > Cc: "r-help@r-project.org > <javascript:_e(%7B%7D,'cvml','r-help@r-project.org');>" < > R-help@r-project.org > <javascript:_e(%7B%7D,'cvml','R-help@r-project.org');>> > Subject: Re: [R] Decision

Re: [R] Decision Tree and Random Forrest

On Thu, 14 Apr 2016, Michael Artz 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

Re: [R] Decision Tree and Random Forrest

- Original message From: Sarah Goslee <sarah.gos...@gmail.com> Date: 4/13/16 8:04 PM (GMT-06:00) To: Michael Artz <michaelea...@gmail.com> Cc: "r-help@r-project.org" <R-help@r-project.org> Subject: Re: [R] Decision Tree and Random Forrest On Wednesday, A

Re: [R] Decision Tree and Random Forrest

On Wednesday, April 13, 2016, Michael Artz 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

Re: [R] 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"

Re: [R] Decision Tree and Random Forrest

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:

Re: [R] Decision Tree and Random Forrest

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),

Re: [R] Decision Tree and Random Forrest

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

Re: [R] Decision Tree and Random Forrest

Also that being said, just because random forest are not the same thing as decision trees does not mean that you can't get decision rules from random forest. On Wed, Apr 13, 2016 at 4:11 PM, Michael Artz wrote: > Ok is there a way to do it with decision tree? I just

Re: [R] Decision Tree and Random Forrest

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

Re: [R] Decision Tree and Random Forrest

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

[R] Decision Tree and Random Forrest

Hi I'm trying to get the top decision rules from a decision tree. Eventually I will like to do this with R and Random Forrest. There has to be a way to output the decsion rules of each leaf node in an easily readable way. I am looking at the randomforrest and rpart packages and I dont see