I'm a newbie in SVM, and have several questions regarding a tool in libsvm.
There's tools/grid.py which tools/README explains as "parameter selection tool for C-SVM classification using 47 the RBF (radial basis function) kernel".
I have 2 questions regarding this tool.
- What this tool does is: given sets of label/feature_parameters, chooses the most "efficient" and "minimum" feature_parameters by performing grid search. Am I correct?
e.g. Given a dataset like following, which the label is only dependent on param1,
label, param1, param2, param3 0 , 0 , 61 , 2 0 , 0 , 92 , 6 1 , 1 , 10 , 32 1 , 1 , 83 , 10
If we apply grid.py to this dataset, does it tell me that most "efficient" (in the way that it precisely identifies the class of a test data) and "minimum" (in the way that only that no trivial parameters are included) parameter is param1.
- If the answer of the question above is YES, how can I know which parameters are efficient and minimum? I see some output files but doens't make sense for me. If it's NO, are there any de facto standard method for doing what I want?