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
------------------------------------------------------------------------------
Free Next-Gen Firewall Hardware Offer
Buy your Sophos next-gen firewall before the end March 2013
and get the hardware for free! Learn more.
http://p.sf.net/sfu/sophos-d2d-feb
_______________________________________________
Scikit-learn-general mailing list
[email protected]
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
