Hi,
The model representation (the tree structure) shouldn’t be affected by the fact
that the input data is sparse or dense.
You might be interested by this issue
https://github.com/scikit-learn/scikit-learn/issues/655.
Best,
Arnaud
On 07 Mar 2014, at 18:13, vamsi kaushik <[email protected]> wrote:
> hi Gael,
> thanks for that.
>
> Firstly i have some issues to clarify before diving into the proposal part.
> I have a potential architecture in mind for the sparse implementation
>
> firstly i feel a separate baseSplitter ( SparseSplitter) must be implemented
> which is inherited by the Sparsebestsplitter etc.
> We can construct algorithms for efficiently extracting random features or
> extracting samples.
>
> But how do we implement a tree that works on sparse data ?
> "The binary tree is represented as a number of parallel arrays. The i-th
> element of each array holds information about the node `i`"
> And i don't think trees work on sparse matrices by standard
>
> Excuse my noobness, but can anyone help me out ?
>
>
> On Thu, Mar 6, 2014 at 12:01 PM, Gael Varoquaux
> <[email protected]> wrote:
> Hi Kaushik,
>
> What I suggest that you do is that you create a page on the wiki for your
> proposal, and you start iterating on it there. You also need to ask for
> feedback on the mailing list with regards to that page and need to find
> two mentors (one mentor and a co mentor) that are willing to mentor you
> and ideally help build your proposal.
>
> Cheers,
>
> Gaël
>
> On Thu, Mar 06, 2014 at 04:45:58AM +0530, vamsi kaushik wrote:
> > Hi,
> > I am kaushik and i would like to submit a proposal for gsoc 2014. I have
> > been
> > working on the sparse matrix implementation in decision trees. I would like
> > to
> > write a proposal regarding the project and i would be glad if anyone could
> > help
> > me out.
>
> > thanks,
> > kaushik varanasi
>
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