1pmish
-luke
> On Aug 3, 2016, at 4:13 PM, Matthieu Brucher
> wrote:
>
> 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 compa
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
>> tricky to achieve.
>> Even with seri
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 tricky to achieve.
Even with serializing the version, if the previous version doesn't
know about the
e and paid a serious lesson for that.
Best,
Shi
-- Forwarded message --
From: Andreas Mueller mailto:t3k...@gmail.com>>
Date: Wed, Aug 3, 2016 at 1:29 PM
Subject: Re: [scikit-learn] Model trained in 0.17 gives entirely different
results in 0.15
To: Scikit-learn user an
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
-- Forwarded message --
From: Andreas Mueller
Date: Wed, Aug 3, 2016 at 1:29 PM
Subject: Re: [scikit-learn] Model trained in 0.17 gives entirely different
results in 0.15
To: Scikit-learn user and developer mailing list
Hi Shi.
In general, there is no guarantee that models built with one
Hi Shi.
In general, there is no guarantee that models built with one version
will work in a different version.
In particular, loading in an older version when built in a newer version
seems something that's tricky to achieve.
We might want to warn the user when doing this. The docs are not ver
Hello,
We trained SVM models in scikit-learn 0.17 and saved it as pickle files.
When loading the models back in a lower version of scikit-learn 0.15, the
outputs are entirely different. Basically for binary classification
problem, for the same test data, it swapped the probabilities and gave an