This is a general problem if the features are not in the same units.
As you saw, PCA assumes that features all have equal importance.
If you want all to have the same weight, you have to rescale (using
StandardScaler for example).
The problem is: it is not clear whether this is the right thing to do.
Maybe the one component just was much more important and the rest was
just noise.
So you have to use your own knowledge of the data - you just have to be
aware which
algorithms make which assumptions.
On 01/10/2013 06:00 PM, paul.czodrow...@merckgroup.com wrote:
Sorry for the confusion, guys.
But I did not scale my features - they contain a wild mixture of values:
- floats ranging from 0 to 1200
- floats ranging from 0 to 60
- integers between 0 and 25
and so on...
My fault!
BTW, I tried to re-run the IRIS example
(http://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_lda.html)
on my data without any preprocessing..
Cheers & Thanks,
Paul
> > I fear that I mixed up my syntax...
>
> Syntax looks good.
>
> If there is one largely predominant component in the data, you should be
> able to see it with your naked eye: all the features should have series
> that look similar to a scaling.
>
> G
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