So far I only have a strong opinion on not relying on the presence of
decision_function or predict_proba to identify a classifier.
Also, is the distinction we seek between classifiers and regressors,
precisely, or between categorical and continuous predictors? (i.e. do we
care that clusterers and classifiers fall together?)
On 29 November 2014 at 14:57, Mathieu Blondel <math...@mblondel.org> wrote:
>
>
> On Sat, Nov 29, 2014 at 11:33 AM, Aaron Staple <aaron.sta...@gmail.com>
> wrote:
>
>> Hi Mathieu,
>>
>> Thanks for the information you’ve provided about the ridge implementation
>> and your suggestions for scoring rankings.
>>
>> First off, I’d like to try and contain the scope of the project I’m
>> working on. Would it be reasonable for me to implement adding get_score to
>> scorer, along with the oob implementation for random forests, first? This
>> discussion of the ridge code seems to have raised a new set of design and
>> implementation questions that might be coded separately.
>>
>
> I understand you but we must also be careful of not creating a half-cooked
> API.
>
>
>>
>> Also, I am coming up to speed on your suggestions regarding support for
>> ranked scoring of regression predictions.
>>
>> My impression is that in sklearn regressors typically implement predict,
>> while classifiers typically implement predict as well as predict_proba
>> and/or decision_function. Currently ThresholdScorer attempts to call
>> decision_function() and if that fails then it calls predict_proba(). What
>> if ThresholdScorer were extended to also call predict() if neither
>> decision_function or predict_proba exists? That way predict() would be
>> called for regressors without an interface change for estimators.
>>
>
> This is a good suggestion. To summarize here are our options:
>
> - introduce a new method predict_score / predict_confidence (pro:
> duck-typing friendly, con: one more method)
> - use isinstance(estimator, RegressorMixin) to detect regressors (pro:
> simple, con: assume inheritance)
> - make decision_function an alias of predict in all regressors (pro:
> simple, con: can no longer detect classifiers with hasattribute(estimator,
> "decision_function"))
> - call predict if neither predict_proba nor decision_function are
> available (pro:simple, con: can't raise an exception for classifiers with
> predict only)
>
> What do people think?
>
> Mathieu
>
>
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