Jsut read the end of the email.
2016-05-17 22:02 GMT+01:00 Jianjian Jin :
> Hi,
>
>Looks like there are too many emails, so I'll have to unsubscribe to
> the mailing list, would you help me with that? Thanks a lot.
>
> best
>
> Jianjian
>
> ___
> sc
I don't even think that squashing them before the merge is actually sound.
You will still need the history of why something happened several years
down the road (and rebasing actually has a similar issue). This bit me
quite often (having just one big commit to analyze after a merge from
ancient VCS
Hi!
The errors are quite small compared to the machine precision. As the
reduction is also an approximation of the underlying manifold, not an
"isotropic" one as well (you can see int he example that red points are
less squashed together than blue ones), you won't have a perfect
reconstruction eit
You can also try one, and if you are stuck, just ask for help. Someone
should be able to help you out ;)
2016-06-18 10:33 GMT+01:00 Sagar Kar :
> Thanks Gael,
> I read it. But I am having hard time finding an issue to work on. Frankly,
> I am unable to understand how to approach the easy issues a
More often than not, forward compatiblity is not possible. I don't think
there are lots of companies doing so, as even backward compatibility is
tricky to achieve.
Even with serializing the version, if the previous version doesn't know
about the additional data structures that have an impact on the
True!
2016-08-03 20:38 GMT+01:00 Andreas Mueller :
>
>
> On 08/03/2016 03:16 PM, Matthieu Brucher wrote:
>
>> More often than not, forward compatiblity is not possible. I don't think
>> there are lots of companies doing so, as even backward compatibility is
>&
Hi all,
I've noticed a few questions online (mainly SO) on TfidfVectorizer speed,
and I was wondering about the global effort on speeding up sklearn.
Is there something I can help on this topic (Cython?), as well as a
discussion on this tough subject?
Cheers,
Matthieu
--
Quantitative analyst, P
Hi qll,
Sorry for the late reply, lots of things to work on currently.
I'll have a look at the roadmap and the pointers to see what could be done
to enhance the situation.
Cheers,
Matthieu
Le lun. 26 nov. 2018 à 20:09, Roman Yurchak via scikit-learn <
scikit-learn@python.org> a écrit :
> Trie
Hi CW,
It's not about the concept of the black box, none of the algorithms in
sklearn are a blackbox. The question is about model validity. Is linear
regression a valid representation of your data? That's what the train/test
answers. You may think so, but only this process will answer it properly.
Hi,
What are you wondering?
The individual tree is weakened by design (accepts more errors), so
indeed, the individual trees are weak learners and the combination of
them (the forest) becomes the strong learner.
You can have a strong tree as well (deeper, more parameters), but
that's not what is s
data_file["data"], this works only if you have such a column as well.
load_csv can perfectly do what you need, but you have to adapt the
script to what you have in the csv (which is something only you
know!).
You need to understand what the different statements are doing; just
as you need to unders
GA are not a machine learning model, they are a way of minimizing a cost
function, so there are probably modules that are dedicated to this
elsewhere.
Matthieu
Le dim. 30 oct. 2022 à 12:19, Thomas Evangelidis a
écrit :
> Hi,
>
> I am not aware of any *official* scikit-learn implementation of a
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