I'd be curious to hear the results of this inquiry.
-Robert
On Mon, Dec 15, 2014 at 10:02 AM, Andy wrote:
> Hey all.
> Afaik we don't currently have continuous integration for OS X.
> A colleague just told me that travis does that in closed beta and you
> just have to ask.
> Olivier, do you wan
When using OneHotEncoder, is it possible to have one integer per feature as the
output, as opposed to binary representation?
Also, when using OneHotEncoder, what would be the method to load data (.csv)
with mixed type (number and categorical)?
Thanks,
--
Thanks all,
I am just thinking to build an anomaly (novelty) detector using one
class SVM with manual inspection to detected anomaly as a feedback to
update the normal model.
i will think of something else :)
Regards,
AdyWP
---
There is a cool new version of Lasvm in vowpal wabbit that I want to
implement, but probably not inside sklearn as it is too fresh.
On 12/15/2014 10:32 AM, Sebastian Raschka wrote:
> Yes, unfortunately that's the nature of SVM. I However, there have been
> implementations with on-line learning
Hey all.
Afaik we don't currently have continuous integration for OS X.
A colleague just told me that travis does that in closed beta and you
just have to ask.
Olivier, do you want to look into that? Otherwise I can.
Cheers,
Andy
--
Yes, unfortunately that's the nature of SVM. I However, there have been
implementations with on-line learning capabilities for SVM that are (promised
to be) nearly as accurate as the batch learning variant. Maybe the partial_fit
could be a useful task for the GitHub "issue" list. Here would be
On 12/13/2014 01:09 AM, He-chien Tsai wrote:
> Thanks for reply. I misused random.seed as it returns None.
> I passed an integer to random_state but it remains that unexpected
> behaviors.
> After I cloned the estimator by sklearn.base.clone,e the result
> becomes reasonable.
>
> clfs = [ (clone(
On 12/14/2014 11:03 PM, Joel Nothman wrote:
> If the estimator supports `partial_fit`, you can use that, repeatedly,
> instead of `fit`.
>
> See documentation:
> http://scikit-learn.org/stable/modules/scaling_strategies.html
> http://scikit-learn.org/stable/auto_examples/cluster/plot_dict_face_pat
