On 01/11/2013 09:53 AM, [email protected] wrote:
Dear Andy & the rest,
by "StandardScaler" => are you talking about the "Scaler" class of the
"preprocessing" module?
Yes. It was renamed but I guess the renaming is only in the next release.
In my case, I used the "preprocessing.scale" routine:
"
X = preprocessing.scale(dataDescrs_array)
"
This should call the same routine, at least that's the way I
understood the documentation.
Correct.
When now performing the PCA, the explained variance of first two
components of the PCA are 0.197 and 0.057
=> My interpretation of this result: for my binary classification
problem ("active" and "inactive") of my samples set, the features make
no clear distinction between the two classes.
How do you draw conclusions about classification from the PCA results?
Have you plotted the 2d data with class labels?
If not, you should do so now ;)
Cheers,
Andy
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