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I am using `geNetClassifier` function of same name package to build a classifier for gene-expression data.
`@classifier` slot of returned function provide information on call as below
`Call:
svm.default(x = t(esetFilteredDataFrame[buildGenesVector, trainSamples, drop = FALSE]), y = sampleLabels[trainSamples], kernel = "linear", probability = TRUE, C = 1)`
`
Parameters:
SVM-Type: C-classification
SVM-Kernel: linear
cost: 1
gamma: 0.002739726
`
I am wondering if it possible somewhat to change SVM-kernal parameter in the `geNetClassifier ` ?
Thanks in advance
Hi Dario,
Thanks a lot for your answer and the the great tip about the `caret` package.
The main reason I am using this package is that it outpust a list of discriminant genes for each of putative sub-classes in my data. Is that an option in the packages you mentioned above?
It's very tempting with all those method for train models aside from all SVM based methods! just for my info, if it has happened to you to try some of those methods, in your experience which one is a better alternative for class discovery in gene expression data? I know SVM, and RandomForest are popular approaches based on literature but I am not sure that the only plausible options one can use
Best,
Hossein
No, the packages I mentioned don't output genes selected based on a multi-class criteria. You would need to write the function yourself. Of course, there are many classifiers you can use and different methods will give better classification performance for different datasets.