ng good.
Cheers,
Michael
On Wed, Aug 8, 2012 at 11:53 AM, Andreas Mueller
wrote:
> Hi Michael.
> Actually that one is on my priority list. But my priority list is long ;)
> Any help is always welcome.
>
> Andy
>
>
> On 08/08/2012 07:41 PM, Michael Waskom wrote:
>
>
Hi,
Do you think multinomial logit via SGD (GH849
https://github.com/scikit-learn/scikit-learn/pull/849) will make it into
0.12? This pull request seems to have stalled, but would be very nice to
have!
Best,
Michael
On Wed, Aug 1, 2012 at 6:52 AM, Gael Varoquaux <
gael.varoqu...@normalesup.org>
Hi all,
I asked a question on metaoptimize about quantitative comparisons between
classifier confusion matrices. If anyone has a good idea and would like to
chime in, it would be much appreciated.
http://metaoptimize.com/qa/questions/9936/good-methods-to-compare-classifier-confusion-matrices
Tha
ta can be dangerous when it comes to
> "interpretation".
> Basically I wouldn't do it if it's a plain logistic regression working
> with voxel
> based features. Now it depends on what you want to say and you might
> find a way, eg. using permutations or bootstrap, to as
Hi all,
I have a LogisticRegression model I'm training in a 3-class scenario. I'd
like to examine the coefficients for the models. As the default for
LogisitcRegression is to do one-vs-all classification, my clf.coef_ array
is shape 3 x nfeat.
My question is how to interpret the sign of the coe
ned from the
training set. But I think it's best to double-check with the experts.
Also, if there's anything you would suggest I do from here to further
elucidate the cause for the structure in bad_pca.png, I'd be happy to
look into it.
Best,
Michael
On Mon, Jan 30, 2012 at 12:3
this will yield a meaningful organization when pooling across
> folds. You probably want to train the PCA on the whole dataset, or did I
> miss something ?
>
> Bertrand
>
> On 01/29/2012 10:38 PM, Michael Waskom wrote:
>> Aha, this does indeed suggest something strange:
>
12 at 1:14 PM, Alexandre Gramfort
wrote:
> hum...
>
> final suggestion: I would try to visualize a 2D or 3D PCA to see if it
> can give me some intuition on what's happening.
>
> Alex
>
> On Sun, Jan 29, 2012 at 9:58 PM, Michael Waskom wrote:
>> Hi Alex,
>>
ggestions:
>
> - do you observe the same behavior with SVC which uses a different
> multiclass strategy?
> - what do you see when you inspect results obtained with binary
> predictions (keeping 2 classes at a time)?
>
> Alex
>
> On Sun, Jan 29, 2012 at 4:59 PM, Michael Wask
ect effect that
> acts as a confound.
>
> again just a thought ready the email quickly
>
> Alex
>
> On Sun, Jan 29, 2012 at 5:39 AM, Michael Waskom wrote:
>> Hi Yarick, thanks for chiming in! I thought about spamming the pymvpa
>> list, but figured one at a time :)
>
100
> TP 100 26
> TN 26 100
> Summary \ Means: -- -- 100 100 37 37 0.79 0.79 0.63 0.63 0.21
> 0.39 0.41
> CHI^2 123.04 p=1.7e-26
> ACC 0.63
> ACC% 63
>
Hi Folks,
I hope you don't mind a question that's a mix of general machine
learning and scikit-learn. I'm happy to kick it over to metaoptimize,
but I'm not 100% sure I'm doing everything "right" from a scikit-learn
perspective so I thought it best to ask here first.
I'm doing classification of f
ve up, it looks like
it will be quite nice when it's all polished!
Michael
On Tue, Oct 4, 2011 at 11:35 PM, Vincent Michel wrote:
> Hi Michael,
>
> The code has been pushed. It still need some work, but you can
> give it a try !
>
> Best,
>
> Vincent
>
>
ago...) and push it during
> the weekend.
> I will keep you inform.
>
> Best,
>
> Vincent
>
>
> http://nipy.sourceforge.net/nipy/stable/index.html
>
> 2011/9/30 Michael Waskom
>
>> Hi all,
>>
>> I know that there are some other people out t
Hi all,
I know that there are some other people out there using scikit-learn for
MVPA with neuroimaging data. Does anyone have an implementation of the
searchlight method (i.e. http://www.ncbi.nlm.nih.gov/pubmed/16537458) using
scikit-learn that they would be willing to share?
Michael
--
Hi Folks,
Just a note that in the install docs it directs you to use "sklearn" with
pip/easy install:
http://scikit-learn.sourceforge.net/stable/install.html#easy-install
When, in fact, one should be searching for "scikit-learn." It doesn't work
with sklearn.
Otherwise, congrats on the release!
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