>Can you try to turn off IDF normalization using `use_idf=False ` in
>the constructor params of your vectorizer and retry (fit + predict) to
>see if it's related to IDF normalization?
>How many dimensions do you have in your fitted model?
https://gist.github.com/3933727
data_vectors.shape = (10361
On Thu, Oct 25, 2012 at 4:15 PM, Gael Varoquaux
wrote:
> Hi David,
>
> Thanks for the heads up. It's good to have you around.
>
> On Thu, Oct 25, 2012 at 04:12:28PM -0400, David Warde-Farley wrote:
>> A workaround would be to check flags in predict() and do the necessary
>> reshuffle if the flags
Hi David,
Thanks for the heads up. It's good to have you around.
On Thu, Oct 25, 2012 at 04:12:28PM -0400, David Warde-Farley wrote:
> A workaround would be to check flags in predict() and do the necessary
> reshuffle if the flags have been botched, but that's kind of a
> maintenance headache.
I
A colleague of mine ran into this this morning, and I've fixed the
upstream bug in NumPy, but I thought I'd let you guys know in case you
want to put in a workaround.
Basically, if you copy.copy or copy.deepcopy a LinearSVC object (and
probably some other objects where the underlying code relies o
2012/10/25 Nikit Saraf :
> Hi
>
> I'm fairly new to the field of Machine Learning and as a result new user of
> scikit-learn. I'm learning about the Map Reduce parallel implementation of
> Machine Learning Algorithms in python. So I was thinking of ways to
> MapReduce the cross-validation. Anyone h
Hi
I'm fairly new to the field of Machine Learning and as a result new user of
scikit-learn. I'm learning about the Map Reduce parallel implementation of
Machine Learning Algorithms in python. So I was thinking of ways to
MapReduce the cross-validation. Anyone having any ideas on how to translate
