> >
> > 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 :(
>
> It just means that you data is not linearly separable when you project
> it onto the first 2 dimensions of PCA.
>
> This is no big deal though. Not all problems are as easy as iris
> classification :)
>
> What you can also try is plot the histograms for each features. For
> feature that are highly non gaussian (e.g. with a long tail), you
> should try to take a sublinear scaling of them: `sign(x_i) *
> np.log1p(x_i)` instead of `x_i` or alternatively `sign(x_i) *
> np.sqrt(x_i)`. If the histogram shows a multimodal profile then maybe
> percentile binning would help too.
although it might be off-topic, but i got stuck in the visualisation
thing..
i would like to end up with a trellis plot (one histogram per feature, all
histograms displayed in a grid of histograms) for my features. plotting
all features into one histogram is getting quite crowded...
from this blogentry:
http://pandasplotting.blogspot.de/2012/06/further-trellis-display-developments.html#!/2012/06/further-trellis-display-developments.html
it looks fantastic, but i'm getting an ImportError:
"
ImportError: cannot import name trellis_display
"
has anyone done something similar?
cheers & a desparate thanks already,
paul
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