Re: [Scikit-learn-general] Multi-target regression
I just had a look at this, and the documentation on
http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
states y should be y : array-like, shape = [n_samples], did I miss
something? I also tried doing it real quick, and it immediately complained
on the input shape.
On Mon, Sep 8, 2014 at 9:59 AM, Giuseppe Marco Randazzo
gmranda...@gmail.com wrote:
Hello,
look in wilkipedia. There is the general algorithm to estimate the beta
coefficient in a simple linear regression trough the Ordinary Least
Squares. All that you need is in the page:
[image: y = X\beta + \varepsilon, \,]
Then...
[image: \hat\beta = (X^TX)^{-1}X^Ty\ .]
Marco
On 08 Sep 2014, at 09:54, Philipp Singer kill...@gmail.com wrote:
Is there a description about this somewhere? I can't find it in the docu.
Thanks!
Am 05.09.2014 um 18:40 schrieb Flavio Vinicius flavio...@gmail.com:
I the case of LinearRegression independent models are being fit for
each response. But this is not the case for every multi-response
estimator. Afaik, the multi response regression forests in sklearn
will consider the correlation between features.
--
Flavio
On Fri, Sep 5, 2014 at 11:03 AM, Philipp Singer kill...@gmail.com wrote:
Hey!
I am currently working with data having multiple outcome variables. So for
example, my outcome I want to predict can be of multiple dimension. One
line of the data could look like the following:
y = [10, 15] x = [13, 735478, 0.555, ...]
So I want to predict all dimensions of the outcome.
I have seen that some algorithms can predict such multiple targets. I have
tried it with LinearRegression and it seems to work fine.
I have not found a clear description of how this works though. Does it fit
one Regression separately for each outcome variable?
Best,
Philipp
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Re: [Scikit-learn-general] Multi-target regression
On Tue, Sep 16, 2014 at 12:43:49PM +0200, Anders Aagaard wrote:
I just had a look at this, and the documentation on http://scikit-learn.org/
stable/modules/generated/sklearn.linear_model.LogisticRegression.html states y
should be y : array-like, shape = [n_samples],
That's a logistic regression. The discussion below is about an ordinary
least square (class LinearRegression).
G
On Mon, Sep 8, 2014 at 9:59 AM, Giuseppe Marco Randazzo gmranda...@gmail.com
wrote:
Hello,
look in wilkipedia. There is the general algorithm to estimate the beta
coefficient in a simple linear regression trough the Ordinary Least
Squares. All that you need is in the page:
y = X\beta + \varepsilon, \,
Then...
\hat\beta = (X^TX)^{-1}X^Ty\ .
Marco
On 08 Sep 2014, at 09:54, Philipp Singer kill...@gmail.com wrote:
Is there a description about this somewhere? I can’t find it in the
docu.
Thanks!
Am 05.09.2014 um 18:40 schrieb Flavio Vinicius flavio...@gmail.com:
I the case of LinearRegression independent models are being fit
for
each response. But this is not the case for every multi-response
estimator. Afaik, the multi response regression forests in sklearn
will consider the correlation between features.
--
Gael Varoquaux
Researcher, INRIA Parietal
Laboratoire de Neuro-Imagerie Assistee par Ordinateur
NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France
Phone: ++ 33-1-69-08-79-68
http://gael-varoquaux.infohttp://twitter.com/GaelVaroquaux
--
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Re: [Scikit-learn-general] Multi-target regression
And after many years of using them both I still get the two confused...
Sorry about the noise! ;)
On Tue, Sep 16, 2014 at 12:47 PM, Gael Varoquaux
gael.varoqu...@normalesup.org wrote:
On Tue, Sep 16, 2014 at 12:43:49PM +0200, Anders Aagaard wrote:
I just had a look at this, and the documentation on
http://scikit-learn.org/
stable/modules/generated/sklearn.linear_model.LogisticRegression.html
states y
should be y : array-like, shape = [n_samples],
That's a logistic regression. The discussion below is about an ordinary
least square (class LinearRegression).
G
On Mon, Sep 8, 2014 at 9:59 AM, Giuseppe Marco Randazzo
gmranda...@gmail.com
wrote:
Hello,
look in wilkipedia. There is the general algorithm to estimate the
beta
coefficient in a simple linear regression trough the Ordinary Least
Squares. All that you need is in the page:
y = X\beta + \varepsilon, \,
Then...
\hat\beta = (X^TX)^{-1}X^Ty\ .
Marco
On 08 Sep 2014, at 09:54, Philipp Singer kill...@gmail.com wrote:
Is there a description about this somewhere? I can't find it in
the
docu.
Thanks!
Am 05.09.2014 um 18:40 schrieb Flavio Vinicius
flavio...@gmail.com:
I the case of LinearRegression independent models are being
fit for
each response. But this is not the case for every
multi-response
estimator. Afaik, the multi response regression forests in
sklearn
will consider the correlation between features.
--
Gael Varoquaux
Researcher, INRIA Parietal
Laboratoire de Neuro-Imagerie Assistee par Ordinateur
NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France
Phone: ++ 33-1-69-08-79-68
http://gael-varoquaux.infohttp://twitter.com/GaelVaroquaux
--
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Anders Aagaard
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Re: [Scikit-learn-general] Multi-target regression
Is there a description about this somewhere? I can’t find it in the docu. Thanks! Am 05.09.2014 um 18:40 schrieb Flavio Vinicius flavio...@gmail.com: I the case of LinearRegression independent models are being fit for each response. But this is not the case for every multi-response estimator. Afaik, the multi response regression forests in sklearn will consider the correlation between features. -- Flavio On Fri, Sep 5, 2014 at 11:03 AM, Philipp Singer kill...@gmail.com wrote: Hey! I am currently working with data having multiple outcome variables. So for example, my outcome I want to predict can be of multiple dimension. One line of the data could look like the following: y = [10, 15] x = [13, 735478, 0.555, …] So I want to predict all dimensions of the outcome. I have seen that some algorithms can predict such multiple targets. I have tried it with LinearRegression and it seems to work fine. I have not found a clear description of how this works though. Does it fit one Regression separately for each outcome variable? Best, Philipp -- Slashdot TV. Video for Nerds. Stuff that matters. http://tv.slashdot.org/ ___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general -- Slashdot TV. Video for Nerds. Stuff that matters. http://tv.slashdot.org/ ___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general -- Want excitement? Manually upgrade your production database. When you want reliability, choose Perforce Perforce version control. Predictably reliable. http://pubads.g.doubleclick.net/gampad/clk?id=157508191iu=/4140/ostg.clktrk ___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
Re: [Scikit-learn-general] Multi-target regression
Hello, look in wilkipedia. There is the general algorithm to estimate the beta coefficient in a simple linear regression trough the Ordinary Least Squares. All that you need is in the page: Then... Marco On 08 Sep 2014, at 09:54, Philipp Singer kill...@gmail.com wrote: Is there a description about this somewhere? I can’t find it in the docu. Thanks! Am 05.09.2014 um 18:40 schrieb Flavio Vinicius flavio...@gmail.com: I the case of LinearRegression independent models are being fit for each response. But this is not the case for every multi-response estimator. Afaik, the multi response regression forests in sklearn will consider the correlation between features. -- Flavio On Fri, Sep 5, 2014 at 11:03 AM, Philipp Singer kill...@gmail.com wrote: Hey! I am currently working with data having multiple outcome variables. So for example, my outcome I want to predict can be of multiple dimension. One line of the data could look like the following: y = [10, 15] x = [13, 735478, 0.555, …] So I want to predict all dimensions of the outcome. I have seen that some algorithms can predict such multiple targets. I have tried it with LinearRegression and it seems to work fine. I have not found a clear description of how this works though. Does it fit one Regression separately for each outcome variable? Best, Philipp -- Slashdot TV. Video for Nerds. Stuff that matters. http://tv.slashdot.org/ ___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general -- Slashdot TV. Video for Nerds. Stuff that matters. http://tv.slashdot.org/ ___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general -- Want excitement? Manually upgrade your production database. When you want reliability, choose Perforce Perforce version control. Predictably reliable. http://pubads.g.doubleclick.net/gampad/clk?id=157508191iu=/4140/ostg.clktrk ___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general -- Want excitement? Manually upgrade your production database. When you want reliability, choose Perforce Perforce version control. Predictably reliable. http://pubads.g.doubleclick.net/gampad/clk?id=157508191iu=/4140/ostg.clktrk___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
