Hi, Sean, My question just rise from the document in R "'Application of Machine Learning to Microarray Data, SVM and friends'. (http://www.bioconductor.org/labMat/pdf/MachLearn.pdf)" This is an excellent paper, which introduce how to use supervised clustering, such as KNN, SVM, and randomForest. However, all the train and test put into them require KNOWN categories. The code evaluates the accuracy by comparing the PREDICT categories of the test (created by supervised clustering) with their KNOWN categories. So I wonder to know how to predict the test WITHOUT KNOWN categories. Xin -----Original Message----- From: Sean Davis [mailto:sdavis2@mail.nih.gov] Sent: 28 July 2004 11:04 To: Liu, Xin Cc: <bioconductor@stat.math.ethz.ch> Subject: Re: [BioC] KNN, SVM, and randomForest - How to predict test without known categories Xin, There is a wealth of information on the bioconductor website, thanks to many generous and brilliant contributors. One such research is in the documentation section under Lab Materials and is titled 'Application of Machine Learning to Microarray Data, SVM and friends'. A PDF is available (http://www.bioconductor.org/labMat/pdf/MachLearn.pdf) and there are lab materials available, including R code. I encourage all users to peruse these resources frequently--I learn something new every time I look. Sean On Jul 28, 2004, at 4:18 AM, Liu, Xin wrote: > In R, before using KNN, SVM, and randomForest, a expreSet is needed to > build, which require the train WITH known catagories and the test WITH > known catagories. However, by definition, in supervised learning you > always train (with known > catagories), then predict the test WITHOUT known catagories. I wonder > how to implement this. Thank you! > > Xin > > > > > > -----Original Message----- > From: Tom R. Fahland [mailto:tfahland@genomatica.com] > Sent: 27 July 2004 18:48 > To: Liu, Xin; bioconductor@stat.math.ethz.ch > Subject: RE: [BioC] KNN, SVM,and randomForest - How to predict samples > without category > > > By definition, in supervised learning you always train (with known > catagories), then run your unbiased data through for prediction. Both > CV > and train/test partitions are good for choosing parameters and > optimizing the algorithms. I have just completed a study predicting > dose > expsoure with good reasults using different algorithms. > Tom > > -----Original Message----- > From: bioconductor-bounces@stat.math.ethz.ch > [mailto:bioconductor-bounces@stat.math.ethz.ch] On Behalf Of Liu, Xin > Sent: Tuesday, July 27, 2004 07:39 > To: bioconductor@stat.math.ethz.ch > Subject: [BioC] KNN, SVM,and randomForest - How to predict samples > without category > > > Dear all, > > Supervised clusterings (KNN, SVM, and randomForest) use test sample set > and train sample set to do prediction. To create the expreSet, the > category is needed for each sample. However sometimes we need to > predict > sample without its category. Anybody has some clue to do this? Thank > you > very much! > > Best regards, > Xin LIU > > > > This e-mail is from ArraGen Ltd\ \ The e-mail and any > files\...{{dropped}} > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor This e-mail is from ArraGen Ltd\ \ The e-mail and any files ...{{dropped}}