ENH: Enable accelerated linear regression for overdetermined systems

[david-cortes-intel]

Description

As a follow-up from oneDAL PR: oneapi-src/oneDAL#2930

The PR above enables fitting linear regressions to overdetermined systems, both in regular regression and multi-output regression. Currently, the sklearnex estimators fall back to scikit-learn for overdetermined systems:

scikit-learn-intelex/sklearnex/linear_model/linear.py

Line 180 in 2fccf44

not is_underdetermined,

But after the PR above, those should be supported in oneDAL too. Hence, this PR enables using oneDAL for those situations and adds tests for them.

One thing to note though: the mechanism in oneDAL is implemented as a fallback from Cholesky - that is, it will first perform a Cholesky factorization, and if that fails, will then use the fallback solver which can handle overdetermined systems. But if e.g. there are more columns that rows, we'll already know beforehand that Cholesky will invariably fail, and the procedure could be made faster if such kind of solver could be requested straight away from oneDAL. Implementation of it as a selectable solver is left for future PRs.

Note that since the oneDAL PR hasn't been merged at the time of writing, this PR will of course end up failing tests, but will keep it here in the meantime.

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