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}}

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}}