Hi, Saddy,
the initial implementation did something like that, however, as far as I can
remember, the “majority vote” was in favor or the “tuples” (we discussed it
somewhere in the pull request I think:
https://github.com/scikit-learn/scikit-learn/pull/4161). The
before-scikit-learn implementation still uses the regular list of classifier
with a naming scheme similar to your suggestion
(http://rasbt.github.io/mlxtend/user_guide/classifier/EnsembleVoteClassifier/).
Best,
Sebastian
> On Apr 19, 2016, at 1:46 PM, saddy <sadm...@gmx.de> wrote:
>
> Am 18.04.2016 um 23:40 schrieb Sebastian Raschka:
>> * If mistakenly not given estimators tuples, it throws some unintuitive
>> error:
>> What did you provide as input? So, you are suggesting to add an additional
>> “if instance” check to throw a more meaningful message? I think that the
>> input doesn’t necessary have to be a list of tuples but just some sort of
>> nested array, e.g.,
>>
>> estimators=[('lr', clf1), ('rf', clf2), …]
>> estimators=[['lr', clf1], ['rf', clf2], …]
>> etc.
> I simply put the estimators into the list: estimators=[clf0, clf1, ...].
> It may be useful if the VotingClassifier auto-generated the names like so:
>
> if type(estimators[0]) is not tuple:
> estimators=[('clf%i' % i, clf) for i, clf in
> enumerate(estimators)] # [('clf0', clf0), ('clf1', clf1)]
>
>
> Kind regards,
>
> Saddy
>
>
> Hi, Saddy,
> thanks for sharing your ideas, I appreciate it. Let’s use the scikit-learn
> mailing list for scikit-learn related discussions in future, though.
>
>> Why? Can't we derive probabilities also when hard-voting? I would give
>> True a chance of 0.8 if 80% of the voters predicted True for this label.
>
> Hm, yeah, I think that could work, however, it would have to be described
> carefully to avoid confusion, i.e., as normalized label frequency or so.
>
>> * n_jobs would be nice
>
> I agree, there’s already a pull request for that; hopefully, it gets polished
> up and merged soon :).
> https://github.com/scikit-learn/scikit-learn/issues/5820
>
>> * If mistakenly not given estimators tuples, it throws some unintuitive
>> error:
>
> What did you provide as input? So, you are suggesting to add an additional
> “if instance” check to throw a more meaningful message? I think that the
> input doesn’t necessary have to be a list of tuples but just some sort of
> nested array, e.g.,
>
> estimators=[('lr', clf1), ('rf', clf2), …]
> estimators=[['lr', clf1], ['rf', clf2], …]
> etc.
>
> Best,
> Sebastian
>
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