The author (Steffen Rendle) is my thesis adviser I can raise the license
issue if there is
serious interests to include LibFM.
On 04/08/2013 02:01 PM, Lars Buitinck wrote:
> Also LibFM may not be redistributed and may not be used commercially
> without striking a deal with the authors first. This
king with n_features > 1000.
Are you taking any measures to avoid "over-fitting by hand". Here I'm
primary
concerned with problems where n_samples (< 50) is small.
Are you looking at the wrong classified examples one by one?
Thanks for sharing.
Immanuel
>
> On Mon, Oct
] and [4] deal with Functional ANOVA decomposition (Still on my
reading list)
Best,
Immanuel
[0] Hastie, T., R. Tibshirani, J. Friedman, and J. Franklin. "The
Elements of Statistical Learning: Data Mining, Inference and
Prediction." /The Mathematical Intelligencer/ 27, no. 2 (2005
> It's related to my new position at ParisTech but image processing and
> ML are taught in different classes.
>
Congratulations on your new position!
Immanuel
--
Got visibility?
Most devs has no i
forbid the user to set alpha to 0 and throw an exception.
This would give a solid hint that the wrong model / solver is used and avoid
any convergence issues.
Best,
Immanuel
>
>> --
>> Olivier
>> http
n either.
Does someone know how to upload the data so that it can be retrieved
using fetch_mldata() ?
Thanks,
Immanuel
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Hey,
the following is from a docstring in mldata:
-
Load the 'leukemia' dataset from mldata.org, which respects the
sklearn axes convention:
>>> leuk = fetch_mldata('leukemia', transpose_data=False)
>>> print(leuk.data.shape[0])
7129
-
according to http://mldata
I would also like to have a high dim regression data set.
2012/5/31 Vlad Niculae :
>
> On May 31, 2012, at 12:42 , Immanuel B wrote:
>
>>> Does N mean n_samples and p n_features?
>> yes
>>
>>> What about number of targets, is it 1 everywhere?
>>
> Does N mean n_samples and p n_features?
yes
>What about number of targets, is it 1 everywhere?
not sure what you mean...
The first table contains binary classification data, in the second table the
number of classes is given by #class.
for the regression problem, I belief, the lpsa variable ha
Hey,
stuff is now on the following wiki page:
https://github.com/scikit-learn/scikit-learn/wiki/Coordinated-descent-in-linear-models-project-discussion,-summer-2012
Immanuel
2012/5/28 Immanuel :
> Hi,
> the doc should now be editable for everyone using the provided link.
> Blog pos
d the time line seems reasonable to
me.
Immanuel
On 05/28/2012 02:20 PM, Alexandre Gramfort wrote:
> hi,
>
> Immanuel can you share the doc with me so I can edit to provide inline
> comments?
>
> A wiki page on github would do the job too. You could copy there your
> proposal
I'm very interested in your opinion on the data set selection and use of vbench.
best,
Immanuel
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Hey all,
it's really exciting to see so much positive feedback.
Thank you all.
@Vlad, David
Nice job! :)
Immanuel
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Hi,
the ROC curve has indeed been extended to the multiclass case.
for example:
A simplified extension of the Area under the ROC to the multiclass domain
http://homepage.tudelft.nl/a9p19/papers/prasa_06_vuc.pdf
I have used the R pROC package for that, maybe that’s an option.
Done, thanks for pointing out the urgency I wasn't aware of it.
2012/4/19 Olivier Grisel :
> Le 19 avril 2012 03:41, Immanuel B a écrit :
>> Hello all,
>>
>> I rewrote the timeline part of my proposal in order to make it better
>> readable and provide clearer de
://docs.google.com/document/d/1q-sTj8kJ-_q_i_UbRxA-_RJEcvocd0M3Y4fHij9byk0/edit
best,
Immanuel
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> No LARS is another way to solve the LASSO regression problem that is
> distinct from the Coordinate Descent method (and from the Stochastic
> Gradient Descent method too).
Thanks, I was trying to make the connection but only found a Cholesky solver. :)
---
Hey Alex,
> a bonus you could add is logistic regression using L1 + L2. as well as
> the support of ElasticNet (also L1 + L2) using the Lars algorithm.
I'm somewhat lost, can you be more specific? Are you referring to strong rule
support?
best,
Immanuel
> The benefit you could expl
Hello all,
here finally is the draft for my proposal.
https://docs.google.com/document/d/1BG7Qmf3yepwkSCngRtJHQjWg2-tX-ltWxbV-goxXudA/edit
Any remarks are greatly appreciated.
best,
Immanuel
--
Better than sec? Nothing
nually is somewhat cumbersome.
best,
Immanuel
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___
Scikit-learn-genera
te a short setup.py as
suggested on the cython page but that produced
a whole bunch of errors.
best,
Immanuel
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> +1 for starting with a first patch on the current CD implementation to
> get familiar with the existing code base.
Just want to let you know that I'm on it, I hope I can write the batch
over the weekend.
>
> As for the content of the proposal itself, it would be good to include
> extensive profi
the upside I will have no other
obligations during the whole GSOC coding time.
best,
Immanuel
coordinate descent methods:
- CDN,
decomposition method solving the sub-problem by Newton direction with
line search
possible speed ups:
- random pe
>hum it's seems surprising that a coordinate descent procedure blows up the
>memory but i'll have to read the paper. When I find the time …
>
>I had more in mind the glmnet approach for multinomial logistic regression
>which scales pretty well AFIAK
These remarks were quite useful to me, thanks. I
2012/3/22 Gael Varoquaux :
> On Thu, Mar 22, 2012 at 10:52:32PM +0100, Immanuel B wrote:
>> I just debased my scikit-learn fork and run the tests in
>> https://github.com/scikit-learn/scikit-learn/tree/master/sklearn/linear_model/tests
>> .
>> They all return with the s
Hello,
I just debased my scikit-learn fork and run the tests in
https://github.com/scikit-learn/scikit-learn/tree/master/sklearn/linear_model/tests
.
They all return with the same error, the tests in the other packages
run just fine.
Can someone reproduce this?
best,
Immanuel
Failure
gularized regression"
http://www-stat.stanford.edu/~tibs/ftp/WittenTibshirani2008.pdf
Best,
Immanuel
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ional
Engineering Science. Currently writing my diploma theses (master
equivalent) on
a bioinformatic topic using machine learning techniques. I took classes
in machine learning,
optimization, stats, data based modelling etc. I worked as student
research assistant, doing implementations
for different
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