[scikit-learn] Can I evaluate clustering efficiency incrementally?
g.lemaitre58 at gmail.com
Fri May 3 04:14:28 EDT 2019
oh sorry, I see now that you mention about evaluating.
On Fri, 3 May 2019 at 10:12, Guillaume Lemaître <g.lemaitre58 at gmail.com>
> You can always predict incrementally by predicting on batches of samples.
> On Fri, 3 May 2019 at 10:05, lampahome <pahome.chen at mirlab.org> wrote:
>> I see some algo can cluster incrementally if dataset is too huge ex:
>> minibatchkmeans and Birch.
>> But is there any way to evaluate incrementally?
>> I found silhouette-coefficient and Calinski-Harabaz index because I don't
>> know the ground truth labels.
>> But they can't evaluate incrementally.
>> scikit-learn mailing list
>> scikit-learn at python.org
> Guillaume Lemaitre
> INRIA Saclay - Parietal team
> Center for Data Science Paris-Saclay
INRIA Saclay - Parietal team
Center for Data Science Paris-Saclay
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