Hi Dave.
It could be related to this bug:
https://github.com/scikit-learn/scikit-learn/issues/1649
But I'm just guessing.
If you could provide some toy data and the resulting models to
reproduce, we could try to hunt
down the problem.
Any help there would be much appreciated.
Cheers,
Andy
On 02/14/2013 03:09 PM, David Reed wrote:
I was hoping I might be able to get some assistance here. I'm working
on a classification problem and have started to see some pretty
large discrepancies between results from Matlab and Sklearn.
The problem arises when doing univariate LDA. My set is fairly large
with an N of 5000, but he class distribution is very poor with the two
classes lying almost on top of one another, and so I expect the
balanced accuracy to be poor.
Doing the classification problem using sklearn I end with a balanced
accuracy of 0.56, using Matlab, I get 0.61. Initially I was getting a
large discrepancy using xval, and so I attributed the bias to this,
but these results are from resubstituion, or testing on the training set.
I would expect for such a problem with LDA, that the solution would be
unique in this case, and it appears that it is not.
Does anyone have any experience with this? Or an ideas as to why this
is happening?
-Dave
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