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 <
[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:
>
> [image: y = X\beta + \varepsilon, \,]
>
> Then...
>
> [image: \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.
> --
> Flavio
>
>
> On Fri, Sep 5, 2014 at 11:03 AM, Philipp Singer <[email protected]> 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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Mvh
Anders Aagaard
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