I'm working with svm() in the svm library. I'm using it to generate a
classifier for four classes. Problem is, I'm not sure how to interpret
the output when I ask for cross-validation. The documentation refers to
"accuracy," but I'm used to "accuracy" for a given category as something
like number.correct/total.placed.in.class. For example, the total
"accuracy" of a model might be
total.correctly.classified/total.number.of.cases. There are lots of
variations, but that's the idea. In all cases, these numbers will be at
most 1, and almost always less. However, I see numbers that look more
like 100*accuracy. Is this a correct interpretation? What am I missing?
--
Kim Elmore, Ph.D. (PP SEL/MEL/Gllider, N5OP, 2nd Class Radiotelegraph, GROL)
"All weather is divided into three parts: Yes, No and Maybe. The greatest of these
is maybe." -- The original Latin appears to be garbled.
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