[jira] [Commented] (FLINK-1727) Add decision tree to machine learning library

["Sachin Goel (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/FLINK-1727?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14567676#comment-14567676 ] 

Sachin Goel commented on FLINK-1727:
------------------------------------

A complete implementation, with both Gini gain and entropy gain, along with Categorical and continuous attributes is present here: https://github.com/apache/flink/pull/710

Two example data sets are also included in the test suite.

> 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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