Dear expert,
I'm trying to do dimensionality reduction using 'pipeline' based on
the following:
>>> from sklearn.pipeline import Pipeline
>>> from sklearn.svm import SVC
>>> from sklearn.decomposition import PCA
>>> estimators = [('reduce_dim', PCA()), ('svm', SVC())]
>>> clf = Pipeline(estimator
Thanks Joel and Mathieu.
I'll start a new project and ask to add a reference in the Wiki.
(hopefully, this week); I don't think all the techniques should be
included, but at least the most popular (ENN, RENN, OSS) / and efficient
ones (SSMA, PSO).
mblondel, you think 'fit_transform' would be bett
+1 to starting a separate project in order to receive early feedback.
Besides popularity and number of citations, an issue is that our API
doesn't currently support instance reduction. We need to decide whether to
introduce a new method (e.g., "reduce" as you did) or use fit_transform (so
far fit_
As if I'd miss a sprint ;)
On Jun 21, 2014 12:09 AM, "Gael Varoquaux"
wrote:
> Yes I believe that our spare bedroom is available at this time.
>
> Worst case, we should also have funding to house you.
>
> Gaël
>
> PS: awesome that you are coming! !@
>
>
> Original message
> Fro
Yes I believe that our spare bedroom is available at this time.
Worst case, we should also have funding to house you.
Gaël
PS: awesome that you are coming! !@
Original message From: Andy
Date:20/06/2014 21:46 (GMT+01:00) To:
scikit-learn-general@lists.sourceforge.net Su
OK thanks everyone, I got something :)
On Jun 20, 2014 9:46 PM, "Andy" wrote:
> Hey Everyone.
>
> Does anyone by any chance have a spare bed / couch?
>
> Cheers,
> Andy
>
> On 06/08/2014 01:47 PM, Alexandre Gramfort wrote:
>
>> hi everyone,
>>
>> time to reactivate this thread...
>>
>> time is ru
Sent you an email - I know of at least one possibility.
On Fri, Jun 20, 2014 at 2:46 PM, Andy wrote:
> Hey Everyone.
>
> Does anyone by any chance have a spare bed / couch?
>
> Cheers,
> Andy
>
> On 06/08/2014 01:47 PM, Alexandre Gramfort wrote:
> > hi everyone,
> >
> > time to reactivate this
Hey Everyone.
Does anyone by any chance have a spare bed / couch?
Cheers,
Andy
On 06/08/2014 01:47 PM, Alexandre Gramfort wrote:
> hi everyone,
>
> time to reactivate this thread...
>
> time is running fast and we should start planning the details for the sprint.
>
> If you can/want to come plea
Hi Dayvid,
For now, a number of projects that follow the scikit-learn interface but
for one reason or another (often just out of scope) are listed at
https://github.com/scikit-learn/scikit-learn/wiki/Third-party-projects-and-code-snippets
.
I would recommend against keeping everything in a scikit
Hi Abijith,
This should get you started:
http://scikit-learn.org/dev/tutorial/text_analytics/working_with_text_data.html
Brian
On 6/20/14, 12:05 PM, Abijith Kp wrote:
> Can anyone help me with the problem of dealing with feature which are
> both strings of varying length(say from 0 to 100-150 c
Hi Joel,
Thanks for your feedback. Let me see if I got this straight,
you think I should open a new repository and then add an entry
in the Wiki?
Do you have an example of some other project that did the same?
How do I organize it, do I start a new project or I build a new project
inside my sklea
Can anyone help me with the problem of dealing with feature which are both
strings of varying length(say from 0 to 100-150 characters) and numbers?
What will be the most widely used techniques in such kind of situations?
And can it be solved using only scikit-learn?
PS: Initially I have to conver
hi,
Nicolas, could you give some numbers on the impact of these different works
to get an idea of which work might have the highest interest for the
sklearn community? do they all scale to medium or large datasets?
is there anybody on the list with experience with these tools?
Best,
Alex
On Fr
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