Am 14.09.2012 14:53, schrieb Andreas Müller:
> Hi Philipp.

Hey Andreas!
> First, you should ensure that the features all have approximately the same 
> scale.
> For example they should all be between zero and one - if the LDA features
> are much smaller than the other ones, then they will probably not be weighted 
> much.

LDA features sum up to 1 for one sample, because they describe the 
probability of one sample to belong to the different topics (in this 
case 500). So basically, they are between 0 and 1.
>
> Which LDA package did you use?

We used Mallet's LDA implementation, because from experience they have 
the most established smoothing processes. http://mallet.cs.umass.edu/

If we just train on the LDA features we btw get reasonable results, a 
bit worse than pure TFIDF.
>
> I am not very experienced with this kind of model, but maybe it would be 
> helpful
> to look at some univariate statistics, like ``feature_selection.chi2``, to see
> if the LDA features are actually helpful.

Yeah, this would be something I could look into. I have already tried to 
to feature selection with chi2 but not actually looked at the specific 
statistics.
>
> Cheers,
> Andy

Regards,
Philipp
>
>
> ----- Ursprüngliche Mail -----
> Von: "Philipp Singer" <kill...@gmail.com>
> An: scikit-learn-general@lists.sourceforge.net
> Gesendet: Freitag, 14. September 2012 13:47:30
> Betreff: [Scikit-learn-general] Combining TFIDF and LDA features
>
> Hey there!
>
> I have seen in the past some few research papers that combined tfidf
> based features with LDA topic model features and they could increase
> their accuracy by some useful extent.
>
> I now wanted to do the same. As a simple step I just attended the topic
> features to each train and test sample with the existing tfidf features
> and performed my standard LinearSVC - oh btw thanks that the confusion
> with dense and sparse is now resolved in 0.12 ;) - on it.
>
> The problem now is, that the results are overall exactly similar. Some
> classes perform better and some worse.
>
> I am not exactly sure if this is a data problem, or comes from my lack
> of understanding of such feature extension techniques.
>
> Is it possible that the huge amount of tfidf features somehow overrules
> the rather small number of topic features? Do I maybe have to some
> feature modification - because tfidf and LDA features are of different
> nature?
>
> Maybe it is also due to the classifier and I need something else?
>
> Would be happy if someone could shed a little light on my problems ;)
>
> Regards,
> Philipp
>
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