On Sun, Sep 9, 2012 at 4:31 PM, Mathieu Blondel <[email protected]>wrote:
> I've just tried scipy.sparse.linalg.lsqr [*] on the full news20 dataset.
> On my box it takes 8 seconds to run with tol=1e-3 and 5 seconds with
> tol=1e-2 without any accuracy loss. It also solves the memory problem
> mentioned by Lars, as it works directly with X and y.
>
> Unlike scipy.linalg.lsqr, scipy.sparse.linalg.lsqr supports a
> regularization term so it can actually be used to implement Ridge. Also,
> despite the name, it supports dense arrays too so it may be worth comparing
> it with solver="dense_cholesky" in the dense case. It cannot be used if
> sample_weight != 1.0 though.
>
> I'll try to send a PR some time this week.
>
Thanks Mathieu, it would be great if you could take a stab at kernel ridge
regression
as well while you're at it ;-)
Paolo
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