Hi Sean.
For linear classifiers I'm not really aware of benefits in doing
mini-batch training, and I don't think it is widely used (someone
correct me if I'm wrong).
Usually we only like to add features that have a clear benefit for the
users, to prevent scikit-learn from becoming bloated.
Do you have a particular use-case where it is important?
Cheers,
Andy
On 05/01/2014 02:04 AM, Sean Violante wrote:
Hi
I was wondering if there is any interest in implementing
minibatch/batch for the SGD algorithm. As I understand it, this is not
implemented
"There is a compromise between the two forms, which is often called
"mini-batches", where the true gradient is approximated by a sum over
a small number of training examples."
http://en.wikipedia.org/wiki/Stochastic_gradient_descent
This would be doing a partial_fit (on small number of training
examples) but updating the weights only after each epoch rather than
after each training sample
as far as I can see it would only require a flag in the sgd_fast.pyx code.
thanks
Sean
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