Hi, > <snip> > > If I instead output the decision values, the whole procedure is > > fully reproducible, i.e. the exact same values are returned when I > > retrain the model. > > By the decision values, you mean the predict labels, right?
The output of decision values can be turned on in the predict.svm, and is, as I have understood, the distance from the data point to the hyperplane. (I should say that my knowledge here is limited to concepts, I know nothing about the details in which this works...). I use these to create ROC curves etc. > > > I have no idea how the probabilities are calculated, but it seems to > > be in this step that the differences arise. In my case, I feel a bit > > hesitant to use them when they differ that much between runs (15% or > > so)... > > I'd find that a bit disconcerting, too. Can you give a sample of your > data + code your using that can reproduce this example? > I have the data at the office, so I can't do that now (at home). > Warning: Brainstorming Below > > If I were to calculate probabilities for my class labels, I'd make the > probability some function of the example's distance from the decision > boundary. > > Now, if your decision boundary isn't changing from run to run (and I > guess it really shouldn't be, since the SVM returns the maximum margin > classifier (which is, by definition, unique, right?)), it's hard to > imagine why these probabilities would change, either ... > > ... unless you're holding out different subsets of your data during > training, or perhaps have a different value for your penalty (cost) > parameter when building the model. I believe you said that you're > actually training the same exact model each time, though, right? Yes, I'm using the exact same data to train each time. I thought this would generate identical models, but that doesn't appear to be the case. > > Anyway, I see the help page for ?svm says this, if it helps: > > "The probability model for classification fits a logistic distribution > using maximum likelihood to the decision values of all binary > classifiers, and computes the a-posteriori class probabilities for the > multi-class problem using quadratic optimization" This is where I realise I'm in a bit over my head on the theroy side - this means nothing to me... > > -steve Thanks again, Anders ______________________________________________ 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.
