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 > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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