It's not a decision tree, but py-earth may also do what you need. It
handles missingness as described in section 3.4 here:
http://media.salford-systems.com/library/MARS_V2_JHF_LCS-108.pdf.
Basically, missingness is considered potentially predictive.
On Thu, Oct 13, 2016 at 11:20 AM, Jeff wrote:
I'm pushing to get py-earth ready for a release, but I'm having an issue
with the check_estimator function on 32 bit windows machines. Here is a
link to the failing build on appveyor:
https://ci.appveyor.com/project/jcrudy/py-earth/build/job/21r6838yh1bgwxw4
It appears that array conversion is p
sting.assert_array_almost_equal(..., precision=2)
>
> or sth like that?
>
> Best,
> Sebastian
>
> > On May 19, 2017, at 6:10 PM, Jason Rudy wrote:
> >
> > I'm pushing to get py-earth ready for a release, but I'm having an issue
> with the check_estimat
Thomas,
This is sort of related to the problem I did my M.S. thesis on years ago:
cross-platform normalization of gene expression data. If you google that
term you'll find some papers. The situation is somewhat different, though,
because with microarrays or RNA-seq you get thousands of data poin
Hi all,
I'm working on updating py-earth for some recent changes in scikit-learn
and cython. It seems like check_estimator has been significantly improved,
and I'm working through making py-earth compliant with it. I've hit the
following issue, though. It seems check_estimator tests score_sampl
Thanks, Joel. From your response I assume that the use of a y argument to
score_samples is not a violation of the sklearn API, so I'll keep the
method and find a workaround for the check_estimator test as it's currently
written. I'll comment on the issue as well.
On Mon, Dec 10, 2018 at 2:58 P
