Hi all,
I am using random forest to do deep learning/feature learning using the
RandomForestEmbedding in scikit-learn. It would be cool to apply
the random forest on the learned features and induced a higher level
representation.
I have actually tried the naive approach of densified the output from
RandomForestEmbedding and feed it back to another one to get the second level
of representation of the same data, and then apply SVM on it. Not only it is
extremely slow, the result become worst.
However, I think sparse matrix support for decision tree is a worthwhile effort
as it enables me to investigate why the result is worst easily.
Just my 2 cents.
Caleb
On Wednesday, January 22, 2014 1:15 PM, Maheshakya Wijewardena
<[email protected]> wrote:
Hi,
I have been using Scikit-learn One hot encoder for data encoding and the
resulting array supports only for a few models such as logistic regression,
SVC, etc. When I convert those sparse matrices with list comprehension or
toarray() function to dense matrices, resulting arrays become too large for
those classifiers such as Decision trees or any other tree based classifier.
I saw a GSOC project idea of implementing this as mentioned here.
https://github.com/scikit-learn/scikit-learn/wiki/Google-summer-of-code-(GSOC)-2014
I'm looking forward to apply for GSOC this year as well, so I would like start
working on this. From where can I get support for this. (There're no possible
mentors assigned for this)
Regards,
Maheshakya
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