Yes that is the method I was using, and its not giving the same results.
I'm going to keep working on getting some sim data.
On Fri, Feb 15, 2013 at 5:01 AM, Andreas Mueller
<[email protected]>wrote:
> On 02/15/2013 02:10 AM, David Reed wrote:
>
> Could you link that?
>
>
> http://www.mathworks.de/products/statistics/examples.html?file=/products/demos/shipping/stats/classdemo.html#3
>
> "The classify function can perform classification using different types
> of discriminant analysis. First classify the data using the default linear
> discriminant analysis (LDA)."
>
> Not sure if that was the same method you are using.
>
>
> 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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>>
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