I dont think this is the problem. My data is definetly oversampled, I have
5000 samples for the 1 feature.
I also should say that the problem that led be to LDA was seeing there was
a large bias between SVM classification accuracy in sklearn and matlab. I
am using the same parameters on both, and again testing on my training.
Using the same univariate data set, I see 0.63 from matlab and 0.58 from
sklearn.
On Thu, Feb 14, 2013 at 9:22 AM, Andreas Mueller
<[email protected]>wrote:
> 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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