Hey Farzana,
The algorithm only keeps one batch in memory at a time. Between processing
over each batch, SGD keeps a set of weights that it alters with each
iteration of a data point or instance within a batch. This set of weights
functions as the persisted state between calls of partial_fit. That
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 wrote:
> On 2019-09-09 12:12, Daniel Sullivan wrote:
> > Hi Farzana,
> >
> >
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
(Sent to wrong mailing list, sorry for duplication)
Hi Chaitanya,
Yes, Stochastic Gradient Descents algorithm logic is written in Cython. The
implementation can be viewed here:
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/linear_model/sgd_fast.pyx
Hope that helps,
Danny
On
