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 ------------------------------------------------------------------------------ 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=157508191&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
