On Sun, Jul 7, 2013 at 11:50 PM, <
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> Today's Topics:
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> 1. Data Compression (Anubhab Baksi)
> 2. Re: Data Compression (Olivier Grisel)
> 3. Questions for plot_forest_iris.py and AdaBoost (Ian Ozsvald)
> 4. AdaBoost and strong vs weak classifiers (Ian Ozsvald)
> 5. RandomForests - where do we select a subset of features
> during fitting? (Ian Ozsvald)
> 6. Defining a Density Estimation Interface (Jacob Vanderplas)
> 7. Re: AdaBoost and strong vs weak classifiers (Olivier Grisel)
>
>
> ----------------------------------------------------------------------
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> Message: 1
> Date: Sun, 7 Jul 2013 21:16:47 +0530
> From: Anubhab Baksi <[email protected]>
> Subject: [Scikit-learn-general] Data Compression
> To: [email protected]
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> Hi,
> I searched for several Data Compression codes in Scikit learn, but I could
> not find it.
>
> Now, can anybody please tell me, are these really implemented?
>
>
> Regards.
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> Message: 2
> Date: Sun, 7 Jul 2013 17:56:32 +0200
> From: Olivier Grisel <[email protected]>
> Subject: Re: [Scikit-learn-general] Data Compression
> To: scikit-learn-general <[email protected]>
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> 2013/7/7 Anubhab Baksi <[email protected]>:
> > Hi,
> > I searched for several Data Compression codes in Scikit learn, but I
> could
> > not find it.
> >
> > Now, can anybody please tell me, are these really implemented?
>
> I am not sure what you really mean by such a generic term as "Data
> Compression" but if this is a name for a specific machine learning
> algorithm then it is not implemented in scikit-learn. BTW most machine
> learning models can be interpreted as doing some sort of lossy
> training set compression but the purpose is generally not to be able
> to "uncompress" the model later to recover the training set but rather
> to use the statistical summary of the training set to be able to make
> useful predictions on any future test set assuming they share the same
> statistical distribution.
>
> If you are interested in lossless compression algorithms in Python
> then you should rather use the gzip or bz2 modules of the standard
> lib:
>
> http://docs.python.org/2/library/archiving.html
>
> --
> Olivier
> http://twitter.com/ogrisel - http://github.com/ogrisel
>
> Actually, I meant algorithms like Huffman Coding, LZW Coding etc. These
are not directly related to machine learning though.
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