Hello -
I have noticed that when I run svm() the order of my data matters. If the
first case in the data frame has y=+1 I get the expected decision rule that
says to classify as +1 if f(x)>0. However, if the first case in the data
frame has y=-1 then apparently the decision rule being used says to
classify as +1 if f(x)<0, and in this case all the coefficients are
negative of their values compared to the first case. So the two
classification rules are equivalent, but is a user really supposed to know
the difference? It is likely they would assume the decision rule is always
to classify as +1 if f(x)>0. Does anyone think the behavior I have noticed
is as intended, or is otherwise benign?
Thank you,
Daniel Jeske
Professor
Department of Statistics
University of California - Riverside
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