`CDClassifier` in my project lightning supports group-lasso for multi-class
classification:
http://www.mblondel.org/lightning/generated/lightning.classification.CDClassifier.html#lightning.classification.CDClassifier
Groups are defined as the class weights for each feature and cannot be
changed.
2014-09-23 23:05 GMT+02:00 Pagliari, Roberto :
> I didn’t realize that. I thought it was the name of the class.
It is when you use the make_pipeline shorthand.
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You can indeed tune parameters of the RF with grid search, and the score
method will be used although you could specify a different task metric to
GridSearchCV's scoring parameter.
On 24 September 2014 07:50, Pagliari, Roberto
wrote:
> I’m a bit confused as to why gridsearchCV is not needed with
I'm a bit confused as to why gridsearchCV is not needed with random forests. I
understand that with RF, each tree will only get to see a partial
representation of the data.
However, if I wanted to tune some parameters of the RF, wouldn't I still need
to do gridsearch? If that is the case, does
I didn’t realize that. I thought it was the name of the class.
Thank you,
From: Eraldo Pomponi [mailto:[email protected]]
Sent: Tuesday, September 23, 2014 4:56 PM
To: [email protected]
Subject: Re: [Scikit-learn-general] name of random forest classifier params in
Dear Roberto,
On Wed, Sep 24, 2014 at 12:14 AM, Pagliari, Roberto wrote:
> I’m using a pipeline with gridsearchcv. I tried this to allow search over
> a range of number of trees
>
>
>
> params = dict(random_forest_classifier__n_estimators=[8, 9, 10, 11])
>
> clf = grid_search.GridSearchCV(my_pip
I'm using a pipeline with gridsearchcv. I tried this to allow search over a
range of number of trees
params = dict(random_forest_classifier__n_estimators=[8, 9, 10, 11])
clf = grid_search.GridSearchCV(my_pipeline, param_grid=params)
but the name 'random_forest_classifier__n_estimators' is not co
I need to update my code to add one-hot-encoded original categorical
features a-la-rule to see if it improves f1 score further or not.
--
Olivier
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Achieve
for that you could take a look at spams
http://spams-devel.gforge.inria.fr (it has python bindings) or use any
general analysis sparsity optimizer and choose the analysis operator to fit
your problem.
hth,
michael
On Tuesday, September 23, 2014, Pedro Cardoso
wrote:
> Thank you for the referenc
for that you could take a look at spams
http://spams-devel.gforge.inria.fr (it has python bindings) or use any
general analysis sparsity optimizer and choose the analysis operator to fit
your problem.
hth,
michael
On Tuesday, September 23, 2014, Pedro Cardoso
wrote:
> Thank you for the referenc
Thank you for the reference.
Do you know of anything with overlapping groups ? This implementation
seems for the non-overlapping case.
Regards,
Pedro
2014-09-23 18:16 GMT+02:00 Michael Eickenberg
:
> yes, but it hasn't been integrated in scikit learn. take a look at
> fabian's coordinate desce
yes, but it hasn't been integrated in scikit learn. take a look at fabian's
coordinate descent implementation here
https://gist.github.com/fabianp/1423373
i think a pr based on this would be very much welcomed
michael
On Tuesday, September 23, 2014, Pedro Cardoso
wrote:
> Hi there.
>
> Was any
Hi there.
Was any work been done for Group Lasso Linear classification.
Thanks for any info.
references :
Jerome Friedman, Trevor Hastie, and Robert Tibshiran. 2010. A note on the
group lasso and a sparse group lasso. Technical report, Stanford
University.
Yogatama, Dani, and Noah A. Smith. "
2014-09-08 17:53 GMT+02:00 Yaroslav Halchenko :
>
> On Mon, 08 Sep 2014, Yaroslav Halchenko wrote:
>
>> hm... actually not clear since it claims that it is because of missing
>> bdepends
>
>> scikit-learn build-depends on missing:
>> - libsvm-dev (>= 2.84.0)
Late reply, but why would it depend on
Thanks, the code and the link will be helpful indeed!
Alright, I am starting to work on the subject and will make a PR in some time.
23.09.2014, 16:17, "Kyle Kastner" :
> I started some code here long ago
> (https://gist.github.com/kastnerkyle/8143030) that isn't really
> finished or cleaned up -
I started some code here long ago
(https://gist.github.com/kastnerkyle/8143030) that isn't really
finished or cleaned up - maybe it can give you some ideas/advice for
implementing? I never got a chance to clean this up for PR, and it
doesn't look like I will have time in the near future so your PR
Hi Alexei,
This is a well-known algorithm. I personnally think that it would be
interesting to see it in scikit-learn. Therefor I would definitely
welcome a pull request implementing it.
Cheers,
Gaël
On Tue, Sep 23, 2014 at 02:44:14PM +0400, Умнов Алексей wrote:
> Hello,
> Is there anyone curr
Hello,
Is there anyone currently working on the K-SVD algorithm? If not, I can
implement it, as I need it for my research anyway and there is already a good
Orthogonal Matching Pursuit implementation in the library.
--
Alexey Umnov
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2014-09-23 6:38 GMT+02:00 c TAKES :
> Thanks! What should be the proper behavior when I run the script I wrote?
A MemoryError. The bug is already fixed in master.
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2014-09-23 6:38 GMT+02:00 c TAKES :
> Thanks! What should be the proper behavior when I run the script I wrote?
>
> I tried uninstalling and reinstalling from pip, but no success (same error),
> so I assume I have to build to get the bleeding edge update.
>
> Probably serves me right for using Win
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