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 <
[email protected]> 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 <
> [email protected]>
> > 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 <[email protected]> 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 <
> [email protected]>:
>
>
> > 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.info http://twitter.com/GaelVaroquaux
>
>
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--
Mvh
Anders Aagaard
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