Sounds good. I have to think about the answers.
I'm trying to find out whether replicating each Xi as pi rows with positive
class and ni rows with negative class corresponds to any unreplicated
version but with sample_weights. Can you confirm and what weights would
pi/ni correspond to?
Am 25.06.2015 11:43 schrieb "Joel Nothman" <joel.noth...@gmail.com>:
> Across models, weights should be implemented such that duplicating samples
> would give identical results to corresponding integer weights. That is true
> here, to my understanding, if we remove the stochasticity such that all
> identical samples have their update occur at once.
>
> On 25 June 2015 at 19:28, Daniel Sullivan <dbsulliva...@gmail.com> wrote:
>
>> Hi Anton,
>>
>> The update for each sample is just multiplied by the sample_weight and
>> the class_weight before it's applied to the weight vector (coef_). So
>> if your sample is [1, 2, 3], your gradient is .1, your weight for this
>> sample is .2, and your eta is 5.0 your weight_vector (coef_) would be
>> updated by [+(1 * .1 * .2 * 5.0), +(2 * .1 * .2 * 5.0), +(3 *.1
>> * .2 * 5.0)]. Does that help?
>>
>> Danny
>>
>> On Thu, Jun 25, 2015 at 10:19 AM, Anton Suchaneck <a.suchan...@gmail.com>
>> wrote:
>> > Hello!
>> >
>> > How can I find out what the precise mathematical treatment of the
>> > sample_weights for SGDClassifier in the partial_fit setting is?
>> >
>> > Cheers,
>> > Anton
>> >
>> >
>> >
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>
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