Hi Stavros,
your idea to add an imputer is really good. Please open a JIRA issue for
that.
You're right that failing fast is usually the better behaviour in case of
an undefined value such as NaN or infinity. Thus, I think it makes sense to
define for the different components their value range an
Btw I think we should add an Imputer if we follow scikit-learn as stated
here for preparing the dataset:
http://scikit-learn.org/stable/modules/preprocessing.html
"Imputation of Missing Values" paragraph. What do you think? Should I add
it as an issue on jira?
The question for NaN also holds for g
Ok cool thnx Till.
On Sun, Feb 12, 2017 at 4:59 PM, Till Rohrmann wrote:
> Hi Stavros,
>
> so far we've sticked mainly to scikit-learn in terms of semantics. Thus, I
> would recommend to follow scikit-learn's approach to handle NaNs.
>
> Cheers,
> Till
>
> On Fri, Feb 10, 2017 at 11:48 PM, Stavr
Hi Stavros,
so far we've sticked mainly to scikit-learn in terms of semantics. Thus, I
would recommend to follow scikit-learn's approach to handle NaNs.
Cheers,
Till
On Fri, Feb 10, 2017 at 11:48 PM, Stavros Kontopoulos <
st.kontopou...@gmail.com> wrote:
> Hello guys,
>
> Is there a story for t
Hello guys,
Is there a story for this (might have been discussed earlier)? I see
differences between scikit-learn and numpy. Do we standardize on
scikit-learn?
PS. I am working on the preprocessing stuff.
Best,
Stavros
