[R] Decision Tree and Random Forrest

[Michael Artz]

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
> >>
> >> "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 1:40 PM, Michael Artz <michaeleartz at gmail.com>
> >> wrote:
> >> > 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 anything yet.
> >> > Mike
> >> >
> >> >         [[alternative HTML version deleted]]
> >> >
> >> > ______________________________________________
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> >
> >
>

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