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https://issues.apache.org/jira/browse/FLINK-1727?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14596087#comment-14596087
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ASF GitHub Bot commented on FLINK-1727:
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Github user thvasilo commented on the pull request:
https://github.com/apache/flink/pull/710#issuecomment-114154438
Helo @sachingoel0101, I've started reviewing this PR. It would help a lot
if you check out these initial comments I made and try to fix similar issues
throughout the PR.
Then we can focus on the logic and design.
Also I would recommend you change the PR's name to "[FLINK-1727] [ml]
Decision tree"
and add [WIP] if you consider this to be a work in progress.
Small question: I assume your are implementing the decision tree as
described in the paper you linked? Could you motivate your decision to use that
algorithm?
> Add decision tree to machine learning library
> ---------------------------------------------
>
> Key: FLINK-1727
> URL: https://issues.apache.org/jira/browse/FLINK-1727
> Project: Flink
> Issue Type: New Feature
> Components: Machine Learning Library
> Reporter: Till Rohrmann
> Assignee: Sachin Goel
> Labels: ML
>
> Decision trees are widely used for classification and regression tasks. Thus,
> it would be worthwhile to add support for them to Flink's machine learning
> library.
> A streaming parallel decision tree learning algorithm has been proposed by
> Ben-Haim and Tom-Tov [1]. This can maybe adapted to a batch use case as well.
> [2] contains an overview of different techniques of how to scale inductive
> learning algorithms up. A presentation of Spark's MLlib decision tree
> implementation can be found in [3].
> Resources:
> [1] [http://www.jmlr.org/papers/volume11/ben-haim10a/ben-haim10a.pdf]
> [2]
> [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.46.8226&rep=rep1&type=pdf]
> [3]
> [http://spark-summit.org/wp-content/uploads/2014/07/Scalable-Distributed-Decision-Trees-in-Spark-Made-Das-Sparks-Talwalkar.pdf]
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