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]
<mailto:[email protected]>> wrote:
matlab doc online says linear classifier is lda by default.
Andrew Winterman <[email protected]
<mailto:[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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