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]] > > > > ______________________________________________ > > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > > https://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > > and provide commented, minimal, self-contained, reproducible code. >[[alternative HTML version deleted]]
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 <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]] >> > >> > ______________________________________________ >> > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see >> > https://stat.ethz.ch/mailman/listinfo/r-help >> > PLEASE do read the posting guide >> http://www.R-project.org/posting-guide.html >> > and provide commented, minimal, self-contained, reproducible code. >> > >[[alternative HTML version deleted]]
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]] >> > >> > ______________________________________________ >> > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see >> > https://stat.ethz.ch/mailman/listinfo/r-help >> > PLEASE do read the posting guide >> > http://www.R-project.org/posting-guide.html >> > and provide commented, minimal, self-contained, reproducible code. > >
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]] > >> > > >> > ______________________________________________ > >> > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > >> > https://stat.ethz.ch/mailman/listinfo/r-help > >> > PLEASE do read the posting guide > >> > http://www.R-project.org/posting-guide.html > >> > and provide commented, minimal, self-contained, reproducible code. > > > > >[[alternative HTML version deleted]]