Thanks for all your answers!
Jason, I think you could be right, but the author wrote in the line above the
code
The mean score and the standard deviation of the score estimate are hence
given by:
So I assume he literally meant standard deviation to show how the scores varies
rather than
This is a very common calculation, you will find it at all of these places (but
only with one standard deviation):
http://scikit-learn.org/stable/auto_examples/randomized_search.html
make html fails with
*Could not import extension sphinx.ext.linkcode (exception: No module
named linkcode)*
How can I get the docs to compile?
A quick google search did not help. IRC had friendly people, but did not
answer my question either. Maybe help here?
Steps:
git clone
With respect to Gaussian processes, there are some good packages in
python already (https://github.com/SheffieldML/GPy,
https://github.com/dfm/george, probably others). In particular, GPy
does not require any other dependencies over and above those already
required by sklearn.
Maybe a reasonable
I think most of the GP related work is deciding what the sklearn compatible
interface should be :) specifically how to handle kernels and try to share
with core codebase.
The HODLR solver of George could be very nice for scalibility but algorithm
is not easy. There are a few other options on that
I have the same feeling.
On Thu, Feb 05, 2015 at 03:56:12PM +, Thomas Johnson wrote:
So I don't really have a 'deep' understanding of deep learning, but aren't
things like Gaussian RBMs becoming obsolete? I thought I read that Hinton said
that the current state-of-the-art is Really Big
So I don't really have a 'deep' understanding of deep learning, but aren't
things like Gaussian RBMs becoming obsolete? I thought I read that Hinton
said that the current state-of-the-art is Really Big networks that just use
standard backprop (plus tricks like dropout). Is that not correct, or is
Is Online low rank factorisation still a vaild idea for this year? As it
was in the last years idea list.
On Thu, Feb 5, 2015 at 9:49 PM, Alexandre Gramfort
alexandre.gramf...@telecom-paristech.fr wrote:
I just looked at the list from last year, and what seems most relevant
still is GMMs,
I think adding partial_fit functions in general to as many algorithms as
possible would be nice
Which could be a project in itself, for someone open to breadth rather than
depth.
On 6 February 2015 at 06:43, Kyle Kastner kastnerk...@gmail.com wrote:
IncrementalPCA is done (have to add
Could it also be accounting for +- ? Standard deviation is one sided right?
On Thu, Feb 5, 2015 at 4:54 PM, Joel Nothman joel.noth...@gmail.com wrote:
With cv=5, only the training sets should overlap. Is this adjustment still
appropriate?
On 6 February 2015 at 06:44, Michael Eickenberg
With cv=5, only the training sets should overlap. Is this adjustment still
appropriate?
On 6 February 2015 at 06:44, Michael Eickenberg
michael.eickenb...@gmail.com wrote:
this is most probably due to the fact that 2 = sqrt(5 - 1), a correction
to variance reduction incurred by the
I just looked at the list from last year, and what seems most relevant
still is GMMs,
and possibly the coordinate descent solvers (Alex maybe you can say what
is left there or
if with the SAG we are happy now?)
there is work coming in coordinate descent and SAG is almost done.
I don't think
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Hmm, not sure why this didn't render:
Hi all,
Sorry for cross posting but we are trying to get as many great submissions
as possible! I'll keep things short with Raniere Silva's summary:
*Long version:*
http://ilovesymposia.com/2015/02/04/call-for-code-nominations-for-elegant-scipy/
.
*Short
On 02/05/2015 01:03 PM, Daniel Sullivan wrote:
I'm still in the process of polishing up SAG, hopefully I can get
something commit-able soon
Sure, no hurry.
My question was more Do we want anything more that is not covered by
your work on SAG ;)
Hi Christof.
Good question. I don't think we came up with a list yet.
I just looked at the list from last year, and what seems most relevant
still is GMMs,
and possibly the coordinate descent solvers (Alex maybe you can say what
is left there or
if with the SAG we are happy now?)
There is still
I'm still in the process of polishing up SAG, hopefully I can get something
commit-able soon
On Thu, Feb 5, 2015 at 12:52 PM, Andy t3k...@gmail.com wrote:
Hi Christof.
Good question. I don't think we came up with a list yet.
I just looked at the list from last year, and what seems most
Congrats Stefan, btw. Now sure where you find the time man.
On Thu, Feb 5, 2015 at 8:28 PM, Juan Nunez-Iglesias jni.s...@gmail.com
wrote:
Hmm, not sure why this didn't render:
Hi all,
Sorry for cross posting but we are trying to get as many great submissions
as possible! I'll keep things
Hi,
I am wondering why the standard deviation of the accuracy estimate is
multiplied by 2 in the example on
http://scikit-learn.org/stable/modules/cross_validation.html; it would be nice
if someone could explain it to me.
The relevant excerpt from the page linked above:
clf =
this is most probably due to the fact that 2 = sqrt(5 - 1), a correction to
variance reduction incurred by the overlapping nature of the folds. the
bootstrap book contains more info on how to calculate these for different
cases of splitting.
hth,
michael
On Thursday, February 5, 2015, Sebastian
IncrementalPCA is done (have to add randomized SVD solver but that should
be simple), but I am sure there are other low rank methods which need a
partial_fit . I think adding partial_fit functions in general to as many
algorithms as possible would be nice
Kyle
On Thu, Feb 5, 2015 at 2:12 PM,
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