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I've been playing around with these methods and trying to get them to produce usable results, but with zero success. As best I can figure, they simply aren't suited to a classification problem like this. After fitting, they all seem to end up predicting a zero for any input (this does, amusingly, give them good accuracy due to the sparseness of RR Lyrae stars, but is obviously a useless result).
The text was updated successfully, but these errors were encountered:
Hi,
Yeah you are right, I tried these methods before with similar results. The
reasons I can think of is that they do not have any weight setting, since
the rr points are less they should be given more weight but I don't see any
such option in elastic nets. I was getting the same results for svm but
there is a setting 'balanced weight' that assigns weight inversely
proportional to the frequency, after which the results were usable.
The other reason might be the data is simply not seperaperble linearly in
the 4d space.
On Dec 7, 2015 2:01 PM, "dcockerham" notifications@github.com wrote:
I've been playing around with these methods and trying to get them to
produce usable results, but with zero success. As best I can figure, they
simply aren't suited to a classification problem like this. After fitting,
they all seem to end up predicting a zero for any input (this does,
amusingly, give them good accuracy due to the sparseness of RR Lyrae stars,
but is obviously a useless result).
—
Reply to this email directly or view it on GitHub #19.
I've been playing around with these methods and trying to get them to produce usable results, but with zero success. As best I can figure, they simply aren't suited to a classification problem like this. After fitting, they all seem to end up predicting a zero for any input (this does, amusingly, give them good accuracy due to the sparseness of RR Lyrae stars, but is obviously a useless result).
The text was updated successfully, but these errors were encountered: