Hey Gene.
I think it depends on what your loss function will be.
How do you measure performance for continuous outputs?
Cheers,
Andy
On 07/02/2013 02:40 PM, Gene Kogan wrote:
Yep, thanks allo. I got the same answer mainly in metaoptimize as
well. I will be using that. Thanks!
best,
gene
On Tue, Jul 2, 2013 at 7:34 PM, Jaques Grobler
<[email protected] <mailto:[email protected]>> wrote:
Didn't see your reply yet, Mathieu :)
Thanks
2013/7/2 Jaques Grobler <[email protected]
<mailto:[email protected]>>
Ah when I looked further I see you got some answers here too
http://metaoptimize.com/qa/questions/13329/regression-task-trained-on-binary-labels
2013/7/2 Jaques Grobler <[email protected]
<mailto:[email protected]>>
I would think that Logistic Regression[1] could apply
here.. You can feed it binary labels and then it will act
as a classifier that will return for each label the
conditional class probability values .
See [2] for scikit-learns implementation
[1] http://en.wikipedia.org/wiki/Logistic_regression
[2]
http://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
Hope it helps :)
2013/7/1 Gene Kogan <[email protected]
<mailto:[email protected]>>
I have a regression task where I have to assign a
continous label between 0 and 1, but my training set
contains only binary labels, 0s and 1s. Should I
treat this as a classification problem and map the
labels to a continous line via some confidence metric
(if it's available) or is there a regression algorithm
which can be trained on binary labels? What
scikits-learn methods will help me achieve this? Thanks!
best,
gene
------------------------------------------------------------------------------
This SF.net email is sponsored by Windows:
Build for Windows Store.
http://p.sf.net/sfu/windows-dev2dev
_______________________________________________
Scikit-learn-general mailing list
[email protected]
<mailto:[email protected]>
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
------------------------------------------------------------------------------
This SF.net email is sponsored by Windows:
Build for Windows Store.
http://p.sf.net/sfu/windows-dev2dev
_______________________________________________
Scikit-learn-general mailing list
[email protected]
<mailto:[email protected]>
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
------------------------------------------------------------------------------
This SF.net email is sponsored by Windows:
Build for Windows Store.
http://p.sf.net/sfu/windows-dev2dev
_______________________________________________
Scikit-learn-general mailing list
[email protected]
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
------------------------------------------------------------------------------
This SF.net email is sponsored by Windows:
Build for Windows Store.
http://p.sf.net/sfu/windows-dev2dev
_______________________________________________
Scikit-learn-general mailing list
[email protected]
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
