[R] Questions on implementing logistic regression

Bert Gunter gunter.berton at gene.com
Tue Mar 5 23:58:00 CET 2013


I may be missing something, but what does this have to do specifically
with R? I believe this is OT here and you need to post elsewhere, e.g.
perhaps on stats.stackexchange.com.

-- Bert

On Tue, Mar 5, 2013 at 1:36 PM, Ivan Li <machinelearning2010 at gmail.com> wrote:
> Hi there,
>
> I am trying to write a tool which involves implementing logistic
> regression. With the batch gradient descent method, the convergence is
> guaranteed as it is a convex problem. However, I find that with the
> stochastic gradient decent method, it typically converges to some random
> points (i.e., not very close to the minimum point resulted from the batch
> method). I have tried different ways of decreasing the learning rate, and
> different starting points of weights. However, the performance (e.g.,
> accuracy, precision/recall, ...) are comparable (to the batch method).
>
> I understand that this is possible, since SGD(stochastic gradient descent)
> uses an approximation to the real cost each step. Does it matter? I guess
> it does since otherwise the interpretation of the weights would not make
> much sense even the accuracy is comparable. If it matters, I wonder if you
> have some suggestions on how to make it converge or getting close to the
> global optimal point.
>
>
>
> Thanks!
>
>         [[alternative HTML version deleted]]
>
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-- 

Bert Gunter
Genentech Nonclinical Biostatistics

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