[BioC] KNN, SVM, and randomForest - How to predict test without known categories

[Liu, Xin]

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 at genomatica.com]
Sent: 27 July 2004 18:48
To: Liu, Xin; bioconductor at 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 at stat.math.ethz.ch
[mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of Liu, Xin
Sent: Tuesday, July 27, 2004 07:39
To: bioconductor at 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}}