Github user sachingoel0101 commented on the pull request:
https://github.com/apache/flink/pull/710#issuecomment-114173765
The fundamental idea for a scalable decision tree algorithm is to reduce
the number of splits required to be checked at every node. Ideally, we'd check
for every value of every attribute, which would lead to a N*D number of checks
at every node [N is the number of instances and D is the dimensionality]. To
bring this down, we convert the values for each attribute into a probability
distribution using a histogram. After this, we can perform as many splits as we
want, not depending on the actual number of training instances. Aside from
this, there is nothing special in the paper which is a diversion from the
standard decision tree algorithm.
I'll update the branch soon to incorporate your comments.
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