you could try some backward feature selection like recursive feature
elimination or just dropping features with neglectible coeficients. group l1
penalty on the weigths would probably be the way to go but we don't have that
...
Ark <[email protected]> schrieb:
>>
>> The size is dominated by the n_features * n_classes coef_ matrix,
>> which you can't get rid of just like that. What does your problem
>look
>> like?
>>
>
>Document classification of ~3000 categories with ~12000 documents.
>The number of features comes out to be 500,000 [in which case the
>joblib
>classifier dumped is 10g]. If I use SelectKbest to select 200000 best
>features
>the size comes down to 4.8g maintain the accuracy to 97%. But I am not
>sure if
>there would be another alternative without sacrificing the accuracy.
>
>
>
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