[BioC] KNN, SVM, and randomForest - How to predict samples without category

Tom R. Fahland tfahland at genomatica.com
Tue Jul 27 19:47:55 CEST 2004

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. 

-----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,

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