Could you link that?
I found a function in Matlab, ClassificationDiscriminant, that performs
exactly the same as Python and R. So what is the real difference between
calling the classify algorithm in Matlab and this ClassificationDiscriminant
function?
On Thu, Feb 14, 2013 at 4:14 PM, <[email protected]> wrote:
> matlab doc online says linear classifier is lda by default.
>
>
>
> Andrew Winterman <[email protected]> schrieb:
>
>> 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]>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]> 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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