Hello all, I just read the release announcement, congratulations! One new caught my attention was: Regression Trees/Forests which support multiple outputs. Can someone point out any reference (papers) which this implementation was based on?
For a while in the past I experimented with the Multivariate random forest which is described here: http://onlinelibrary.wiley.com/doi/10.1002/widm.12/abstract. Basically, the idea is that when choosing the feature to split the algorithm accounts for the covariance matrix of response variables*. I would like to know if if sklearn's implementation follow's the same approach. *For example, the squared Mahalanobis distance could be used for regression. This is the same as squared error with one output only. -- Flavio ------------------------------------------------------------------------------ Live Security Virtual Conference Exclusive live event will cover all the ways today's security and threat landscape has changed and how IT managers can respond. Discussions will include endpoint security, mobile security and the latest in malware threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
