[scikit-learn] How does multiple target Ridge Regression work in scikit learn?

[Carol Duncan]

      From: bthirion <bertrand.thirion at inria.fr>
 To: scikit-learn at python.org 
 Sent: Wednesday, May 2, 2018 12:07 PM
 Subject: Re: [scikit-learn] How does multiple target Ridge Regression work in scikit learn?
   
 The alpha parameter is shared for all problems; If you wnat to use differnt parameters, you probably want to perform seprate fits.
 Best,
 
 Bertrand
 
 On 02/05/2018 13:08, Peer Nowack wrote:
  
   Hi all, I am struggling to understand the following: Scikit-learn offers a multiple output version for Ridge Regression, simply by handing over a 2D array [n_samples, n_targets], but how is it implemented? http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html Is it correct to assume that each regression for each target is independent? Under these circumstances, how can I adapt this to use individual alpha regularization parameters for each regression? If I use GridSeachCV, I would have to hand over a matrix of possible regularization parameters, or how would that work? Thanks in advance - I have been searching for hours but could not find anything on this topic.  Peter
  
  
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