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 docum
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 regre
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 in
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 wrote:
> Is there a description about t
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 :
> 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 mult
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, Philip
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.
