Hi,
As a simple test, I was curious to see how much better a multivariate
classification test (2 or more dimensions/features) would be compared to a
univariate classification test (1 dimension/feature). In the univariate case,
can someone help me understand why LinearNuSVMC would differ from RbfNuSVMC?
CV: 79.28% (RbfNuSVMC)
CV: 66.42% (LinearNuSVMC)
We know from a logistic regression that this particular feature can predict our
two conditions with ~80% accuracy. If the SVM classifier only has a single
dimension to work with, should linear and RBF differ this much? I was under the
impression that, given a single dimension, both methods would only find the
best point on that dimension that discriminates the classes.
Details on the dataset are printed below:
Dataset / float64 140 x 1
uniq: 140 chunks 2 labels
stats: mean=0.256292 std=0.231866 var=0.0537616 min=0 max=1
No details due to large number of labels or chunks. Increase maxc and maxl if
desired
Summary per label across chunks
label mean std min max #chunks
0 0.443 0.497 0 1 62
1 0.557 0.497 0 1 78
To account for the unbalanced labels, I'm using nperlabel='equal' in my
splitter.
cv = CrossValidatedTransferError(
TransferError(clf),
NFoldSplitter(nperlabel='equal'),
enable_states=['confusion'])
Thanks!
David
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