I have decided to improve decision tree based on map reduce.
My question is which parameter I have to focus for the same?
Information gain, entropy???????
On Fri, Feb 12, 2016 at 12:55 PM, Chirag Nagpal <chiragnagpa...@hotmail.com>
wrote:
> Hi Chintan
>
>
> What exactly are you referring to when you mean 'Big Data Research in
> Decision Tree'.
>
>
> Scikit learn has a very easy to use interface for applying Random Forest
> on datasets, and is ideal for that. It also supports certain improved RFs
> like boosted, extreme etc.
>
>
> Are you looking to do some fundamental research/improvement on Random
> Forest itself? Like some new method of Bagging/ Boosting etc ? Then I do
> not think Scikit Learn would be a right place to look at. However probably
> one can explore if you have some existing code, which you want to add to
> Scikit.
>
>
> PS. Try not to use Big Data too often, the buzzword gets misused a lot.
> Lets just call it Machine Learning
>
>
> Thanks
>
>
> Chirag Nagpal
>
>
>
>
> ------------------------------
> *From:* Chintan Bhatt <bhattchinta...@gmail.com>
> *Sent:* 12 February 2016 12:30
> *To:* scikit-learn-general@lists.sourceforge.net
> *Subject:* [Scikit-learn-general] Regarding Decision Tree in Scikit-Learn
>
> Hi,
> Is anyone working on big data research in Decision Tree using
> scikit-learn?
>
> --
> -----
> Regards,
> PROF. CHINTAN BHATT <http://in.linkedin.com/pub/chintan-bhatt/22/b31/336>
> PhD*, Computer Science
> https://sites.google.com/a/ecchanga.ac.in/chintan/
>
>
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--
-----
Regards,
PROF. CHINTAN BHATT <http://in.linkedin.com/pub/chintan-bhatt/22/b31/336>
PhD*, Computer Science
https://sites.google.com/a/ecchanga.ac.in/chintan/
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