search for: pending_chores

...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 <michaeleart...

...ict >> 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 w...

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 ru...

...ve 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...

...t 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...

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

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