btw you could also use a different multiclass strategy like error correcting
output codes (exists in sklearn) or a binary tree of classifiers (would have to
implement yourself)
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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