Hi Farzana, Do you have a specific question about partial_fit? Essentially it works the same as the fit method, but the weights are preserved between calls. Within the partial fit and fit methods, the model makes an estimate based on the single data point and adjusts the weights proportionally based on the difference between the estimate and the target. How much the weights are changed depends on the loss function and learning rate you specify.
On Mon, Sep 9, 2019 at 1:32 PM Farzana Anowar <fad...@uregina.ca> wrote: > 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 > >> _______________________________________________ > >> scikit-learn mailing list > >> scikit-learn@python.org > >> https://mail.python.org/mailman/listinfo/scikit-learn > > _______________________________________________ > > scikit-learn mailing list > > scikit-learn@python.org > > https://mail.python.org/mailman/listinfo/scikit-learn > 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 > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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