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 [[alternative HTML version deleted]]
So. Given that the second and third panels of the first figure in the first link I gave show a decision tree with decision rules at each split and the number of samples at each direction, what _exactly_ is your problem? On Wednesday, April 13, 2016, Michael Eugene <fartzy at hotmail.com> wrote:> 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 > > > -------- Original message -------- > From: Sarah Goslee <sarah.goslee at gmail.com > <javascript:_e(%7B%7D,'cvml','sarah.goslee at gmail.com');>> > Date: 4/13/16 8:04 PM (GMT-06:00) > To: Michael Artz <michaeleartz at gmail.com > <javascript:_e(%7B%7D,'cvml','michaeleartz at gmail.com');>> > Cc: "r-help at r-project.org > <javascript:_e(%7B%7D,'cvml','r-help at r-project.org');>" < > R-help at r-project.org > <javascript:_e(%7B%7D,'cvml','R-help at r-project.org');>> > Subject: Re: [R] Decision Tree and Random Forrest > > > > On Wednesday, April 13, 2016, Michael Artz <michaeleartz at gmail.com > <javascript:_e(%7B%7D,'cvml','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> 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> > 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 > >> >> > > > > -- > Sarah Goslee > http://www.stringpage.com > http://www.sarahgoslee.com > http://www.functionaldiversity.org >-- Sarah Goslee http://www.stringpage.com http://www.sarahgoslee.com http://www.functionaldiversity.org [[alternative HTML version deleted]]
I need the output to have groups and the probability any given record in that group then has of being in the response class. Just like my email in the beginning i need the output that looks like if A and if B and if C then %77 it will be D. The examples you provided are just simply not similar. They are different and would take interpretation to get what i need. On Apr 14, 2016 1:26 AM, "Sarah Goslee" <sarah.goslee at gmail.com> wrote:> So. Given that the second and third panels of the first figure in the > first link I gave show a decision tree with decision rules at each split > and the number of samples at each direction, what _exactly_ is your > problem? > > > > On Wednesday, April 13, 2016, Michael Eugene <fartzy at hotmail.com> wrote: > >> 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 >> >> >> -------- Original message -------- >> From: Sarah Goslee <sarah.goslee at gmail.com> >> Date: 4/13/16 8:04 PM (GMT-06:00) >> To: Michael Artz <michaeleartz at gmail.com> >> Cc: "r-help at r-project.org" <R-help at r-project.org> >> Subject: Re: [R] Decision Tree and Random Forrest >> >> >> >> 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> 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> >> 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 >> >> >> >> >> >> >> -- >> Sarah Goslee >> http://www.stringpage.com >> http://www.sarahgoslee.com >> http://www.functionaldiversity.org >> > > > -- > Sarah Goslee > http://www.stringpage.com > http://www.sarahgoslee.com > http://www.functionaldiversity.org >[[alternative HTML version deleted]]
Since you only have 3 predictors, each categorical with a small number of
categories, you can use expand.grid to make a data.frame containing all
possible combinations and give that the predict method for your model to
get all possible predictions.
Something like the following untested code.
newdata <- expand.grid(
Humidity = levels(Humidity), #(High, Medium,Low)
Pending_Chores = levels(Pending_Chores), #(Taxes, None, Laundry,
Car Maintenance)
Wind = levels(Wind)) # (High,Low)
newdata$ProbabilityOfPlayingGolf <- predict(fittedModel,
newdata=newdata)
Bill Dunlap
TIBCO Software
wdunlap tibco.com
On Fri, Apr 15, 2016 at 3:09 PM, Michael Artz <michaeleartz at gmail.com>
wrote:
> I need the output to have groups and the probability any given record in
> that group then has of being in the response class. Just like my email in
> the beginning i need the output that looks like if A and if B and if C then
> %77 it will be D. The examples you provided are just simply not similar.
> They are different and would take interpretation to get what i need.
> On Apr 14, 2016 1:26 AM, "Sarah Goslee" <sarah.goslee at
gmail.com> wrote:
>
> > So. Given that the second and third panels of the first figure in the
> > first link I gave show a decision tree with decision rules at each
split
> > and the number of samples at each direction, what _exactly_ is your
> > problem?
> >
> >
> >
> > On Wednesday, April 13, 2016, Michael Eugene <fartzy at
hotmail.com> wrote:
> >
> >> 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
> >>
> >>
> >> -------- Original message --------
> >> From: Sarah Goslee <sarah.goslee at gmail.com>
> >> Date: 4/13/16 8:04 PM (GMT-06:00)
> >> To: Michael Artz <michaeleartz at gmail.com>
> >> Cc: "r-help at r-project.org" <R-help at
r-project.org>
> >> Subject: Re: [R] Decision Tree and Random Forrest
> >>
> >>
> >>
> >> 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> 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>
> >> 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
> >> >> >>
> >>
> >>
> >>
> >> --
> >> Sarah Goslee
> >> http://www.stringpage.com
> >> http://www.sarahgoslee.com
> >> http://www.functionaldiversity.org
> >>
> >
> >
> > --
> > Sarah Goslee
> > http://www.stringpage.com
> > http://www.sarahgoslee.com
> > http://www.functionaldiversity.org
> >
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
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> PLEASE do read the posting guide
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> and provide commented, minimal, self-contained, reproducible code.
>
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