Logistic regression can be used as a linear classifier. Maybe that's
matlab's linear classifier?
On Thursday, February 14, 2013, David Reed wrote:
> I was mistaken, R is providing the exact same results as Python. Is
> there a difference between a linear classifer and LDA? Matlab never uses
> the words Linear Discriminant Analysis, its just says linear classifier,
> but is giving different results than these other two software packages.
>
>
> On Thu, Feb 14, 2013 at 1:19 PM, David Reed
> <[email protected]<javascript:_e({}, 'cvml', '[email protected]');>
> > wrote:
>
>> I don't think I can provide the data, but I'm trying to create some
>> simulated data that produces a similar difference.
>>
>> I was just messing with R, and for LDA got a different result from the
>> other 2. I wonder if its just something I am doing wrong.
>>
>>
>>
>> On Thu, Feb 14, 2013 at 10:07 AM, Andreas Mueller <
>> [email protected] <javascript:_e({}, 'cvml',
>> '[email protected]');>> wrote:
>>
>>> On 02/14/2013 04:04 PM, Andreas Mueller wrote:
>>> > On 02/14/2013 03:59 PM, David Reed wrote:
>>> >> 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.
>>> >>
>>> > Which version of scikit-learn are you using? Are you using sparse
>>> matrices?
>>> > There was a weird bug in using sparse matrices and SVMs in an earlier
>>> > version of sklearn.
>>> > If there is still a discrepancy in either of the algorithms, we must
>>> > investigate!
>>> >
>>> Would it be possible to provide your data or even better, some small
>>> dataset
>>> to reproduce the discrepancy?
>>>
>>>
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>>
>>
>
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