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 <[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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>>
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>
>
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