On 2019-09-09 12:12, Daniel Sullivan wrote:
Hi Farzana,
If I understand your question correctly you're asking how the SGD
classifier works incrementally? The SGD algorithm maintains a single
set of weights and iterates through all data points one at a time in a
batch. It adjusts its weights on each iteration. So to answer your
question, it trains on each instance, not on the batch. However, the
algorithm can iterate multiple times through a single batch. Let me
know if that answers your question.
Best,
Danny
On Mon, Sep 9, 2019 at 11:56 AM Farzana Anowar <fad...@uregina.ca>
wrote:
Hello Sir/Madam,
I subscribed to the link you sent me.
I am posting my question again:
This Is Farzana Anowar, a Ph.D. candidate in University of Regina.
Currently, I'm working to develop a model that learns incrementally
from
non-stationary data. I have come across an Incremental library in
sci-kit learn that actually allows to do that using partial_fit. I
have
searched a lot for the detailed information about this 'incremental'
library and 'partial_fit', however, I couldn't find any.
It would be great if you could provide me with some detailed
information
about these two regarding how they actually work. For example, If we
take SGD as a classifier, the incremental library will allow me to
take
chunks/batches of data. My question is: Do this incremental library
train (using parial_fit) the whole batch at a time and then produce
a
classification performance or it takes a batch and trains each
instance
at a time from the batch.
Thanks in advance!
--
Regards,
Farzana Anowar
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Hello Daniel,
Thank you so much! I think your clarification makes sense. So, whatever
batches I am passing through the classifier it will train each instance
through a single batch.
I was just wondering if you could give me some information about
partial_fit. Just for your reference, I was having a look at this code.
https://dask-ml.readthedocs.io/en/latest/incremental.html
Thanks!
--
Regards,
Farzana Anowar
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