On 7 May 2016 at 19:12, Matthias Feurer <feur...@informatik.uni-freiburg.de>
wrote:
> 1. Return the fit and predict time in `grid_scores_`
>
This has been proposed for many years as part of an overhaul of
grid_scores_. The latest attempt is currently underway at
https://github.com/scikit-learn/scikit-learn/pull/6697, and has a good
chance of being merged.
> 2. Add distribution objects to scikit-learn which have get_params and
> set_params attributes
>
Your use of get_params to perform serialisation is certainly not what
get_params is designed for, though I understand your use of it that way...
as long as all your parameters are either primitives or objects supporting
get_params. However, this is not by design. Further, param_distributions is
a dict whose values are scipy.stats rvs; get_params currently does not
traverse dicts, so this is already unfamiliar territory requiring a lot of
design, even once we were convinced that this were a valuable use-case,
which I am not certain of.
> 3. Add get_params and set_params to CV objects
>
get_params and set_params are intended to allow programmatic search over
those parameter settings. This is not often what one does with the
parameters of CV splitting methods, but I acknowledge that supporting this
would not be difficult. Still, if serialisation is the purpose of this,
it's not really the point.
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