Cool, thanks for feedback!
Any outstanding PRs addressing something like this or anyone on this list
been thinking of/working on solutions?
I imagine it might be implemented as a step in a pipeline (eg.
FeatureRemover()) and be generally applicable / potentially benefit many
sklearners. Not sure if it could be compatible with HashingVectorizer though
On Mon, Mar 14, 2016 at 7:20 PM, Joel Nothman <joel.noth...@gmail.com>
wrote:
> Currently there is no automatic mechanism for eliminating the generation
> of features that are not selected downstream. It needs to be achieved
> manually.
>
> On 15 March 2016 at 08:05, Philip Tully <tu...@csc.kth.se> wrote:
>
>> Hi,
>>
>> I'm trying to optimize the time it takes to make a prediction with my
>> model(s). I realized that when I perform feature selection during the
>> model fit(), that these features are likely still computed when I go
>> to predict() or predict_proba(). An optimization would then involve
>> actually eliminating those features that aren't selected from my
>> Pipeline altogether, instead of just selecting them.
>>
>> Does sklearn already do this automatically? Or does this readjustment
>> need to be done manually before serialization?
>>
>> thanks,
>> Philip
>>
>>
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>
>
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