Thanks Ronnie for pointing out the exact method in the scikit-learn 
library.  Yes, that is exactly what I was asking how does the rescaling 
of features affect the gradient descent algorithm. Since, stochastic 
gradient descent is an algorithm which is used in machine learning quite 
a lot. It will be good to understand how its performance is affected 
after rescaling features.

Jaques, I am having some trouble running the example. But yes it will be 
good if we can have gui example.

On 25-04-2013 19:12, [email protected] 
wrote:
> Date: Thu, 25 Apr 2013 09:10:35 -0400
> From: Ronnie Ghose<[email protected]>
> Subject: Re: [Scikit-learn-general] Effects of shifting and scaling on
>       Gradient Descent
> To:[email protected]
> Message-ID:
>       <CAHazPTmZX1dmMT1Mm_hTQjyyB8aV5C=5rd-l0dwcvzmdlxf...@mail.gmail.com>
> Content-Type: text/plain; charset="iso-8859-1"
>
> I think he means what increases/benefits do you get from rescaling features
> e.g. minmax or preprocessing.scale
> On Thu, Apr 25, 2013 at 02:09:13PM +0200, Jaques Grobler wrote:
>>> > >I also think it will be great to have this example on the website.
>> >Do you mean like an interactive example that works similiar to the SVM
>> >Gui example , but for understand the effects shifting and scaling of
>> >data has on the rate of convergence of gradient descent and the surface
>> >of the cost function?
> This is out of scope for the project: scikit-learn is a machine learning
> toolkit. Gradient descent is a general class of optimization algorithms.
>
> Ga?l

-- 
sp


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