Actually, from the numpy docs, the ddof=1 for np.std doesn't make it
unbiased. There's a whole wikipedia article on calculating the unbiased
standard deviation, and it seems to be different for the normal
distribution than for others and involves the gamma function--the advice
from the wiki is not to worry about it.
http://en.wikipedia.org/wiki/Unbiased_estimation_of_standard_deviation
However, it seems that some people define standardization as having a zero
mean and unit _variance_, which numpy actually supports and is unbiased for
iid samples. So maybe dividing by the variance and giving the flags
with_var='population', 'sample', or None is the better solution.
Wikipedia's article on feature scaling defines it as zero-mean and unit
variance, but then gives the advice to divide by the standard deviation.
Dividing by std seems like the wrong advice.
http://en.wikipedia.org/wiki/Feature_scaling
Doug
On Tue, Nov 6, 2012 at 7:11 AM, Lars Buitinck <l.j.buiti...@uva.nl> wrote:
> 2012/11/6 Olivier Grisel <olivier.gri...@ensta.org>:
> > None, False: no stdev
> > True, "pop": population stdev
> > "sample": sample stdev
> >
> > +1 but with "population" instead of "pop".
>
> Alright :)
>
> --
> Lars Buitinck
> Scientific programmer, ILPS
> University of Amsterdam
>
>
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