Hi Joseph,
In theory, you should be able to take any classifier in sklearn and base
your implementation off that. That said, there are a few caveats. Some
classifiers are older, before coding was more formalised. Others have a lot
of cython code hooks, and can be difficult to read. That all said,
Is this the right place to ask? I'm just going to send in a pull
request if nobody has any suggestions.
j
On Fri, Jan 31, 2014 at 7:10 PM, Joseph Perla wrote:
> I love SciKit and I'm going to contribute an SGD classifier for
> semi-supervised problems.
>
> I already read through all the contribut
> The basic problem here was converting back from 2D array to 3D or 4D
> array which is handled by NiftiMasker inside Nilearn.
That's what I thought.
Good that it works for you. We need to keep working on it to do a 0.1
release.
Cheers,
Gaël
---
Thanks, sorry for the vague and general problem. Yes Nilearn solved everything,
although it is still under development but examples are fine and one can do
everything based on the examples available. The basic problem here was
converting back from 2D array to 3D or 4D array which is handled by N
> I understand that scikit-learn is a general purpose tool, however here
> I appreciate if you could forward me to a webpage, tutorial , and etc
> to be able to understand the basis of my problem. I work on 3D Images
> of MRI (or 4D). A typical problem, for example, is when I use PCA
> decompositio
On Sat, Feb 01, 2014 at 04:46:08PM +0100, Peter Prettenhofer wrote:
> sorry but I didn't find a dedicated joblib mailing list and since most of the
> joblib contributors hang around here I thought I give it a shot.
It's http://librelist.com/browser/joblib/
job...@librelist.com
> When using compre
Hi list,
sorry but I didn't find a dedicated joblib mailing list and since most of
the joblib contributors hang around here I thought I give it a shot.
I'm using joblib to dump scikit-learn RF models. When using compression is
the output always guaranteed to be stored in a single file? I looked a
