Dear Andy,
> 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?
Yep, I did so, and there was no clear differentiation between the two
classes - at least not as clear as in the Iris show case!
BTW: When doing a RandomizedPCA, the explained variance of the first
component increase to 78%
* Turning whiten on or off has more or less no influence on the explained
variance.
* However, plotting with class labels on => again no clear differentiation
between the two classes :(
Thanks & Cheers,
Paul
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