Gents: This discussion is now off-topic here, I believe. Please take it private or move to somewhere else more appropriate (SO, maybe).
Cheers, Bert On Thu, Dec 5, 2013 at 8:32 AM, Lopez, Dan <lopez...@llnl.gov> wrote: > Hi Andy, > > I have used predict before and in fact when I do that to the train set I get > a perfect model (i.e. ROC right angle curve in upper left quadrant) which > just looks like it overfit the data. > This is not the case with the test set were I get auc of .77. > > I wanted to attempt a couple of calibration techniques I learned from Max > Kuhn's Applied Predictive Modeling book. He uses a train set to do this. But > with what I have now with the train set there is nothing to calibrate. > > That's why I thought I would use the original probabilities from the > randomForest model that was used to create fm$predicted (fm is my > randomForest model). > > I am still fairly new at predictive modeling and it could be the case that > maybe I am not understanding something basic here. > > Thanks. > Dan > > -----Original Message----- > From: Liaw, Andy [mailto:andy_l...@merck.com] > Sent: Monday, December 02, 2013 8:40 AM > To: arun; R help; Lopez, Dan > Subject: RE: [R] How do I extract Random Forest Terms and Probabilities? > > #2 can be done simply with predict(fmi, type="prob"). See the help page for > predict.randomForest(). > > Best, > Andy > > > -----Original Message----- > From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On > Behalf Of arun > Sent: Tuesday, November 26, 2013 6:57 PM > To: R help > Subject: Re: [R] How do I extract Random Forest Terms and Probabilities? > > > > Hi, > For the first part, you could do: > > fmi2 <- fmi > attributes(fmi2$terms) <- NULL > capture.output(fmi2$terms) > #[1] "Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width" > > A.k. > > On Tuesday, November 26, 2013 3:55 PM, "Lopez, Dan" <lopez...@llnl.gov> wrote: > Hi R Experts, > > I need your help with two question regarding randomForest. > > > 1. When I run a Random Forest model how do I extract the formula I used > so that I can store it in a character vector in a dataframe? > For example the dataframe might look like this if I am running models using > the IRIS dataset #ModelID,Type, > > #001,RF,Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width > > fmi<-randomForest(Species~.,iris,mtry=3,ntry=500) > #I know one place where the information is in fmi$terms but not sure how to > extract just the formula info. Or perhaps there is somewhere else in fmi that > I could get this? > > > 2. How do I get the probabilities (probability-like values) from the > model that was run? I know for the test set I can use predict. And I know to > extract the classifications from the model I use fmi$predicted. But where are > the probabilities? > > > Dan > Workforce Analyst > HRIM - Workforce Analytics & Metrics > LLNL > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > 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. > > > ______________________________________________ > R-help@r-project.org mailing list > 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. > Notice: This e-mail message, together with any attachme...{{dropped:10}} > > ______________________________________________ > R-help@r-project.org mailing list > 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. -- Bert Gunter Genentech Nonclinical Biostatistics (650) 467-7374 ______________________________________________ R-help@r-project.org mailing list 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.
