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From "Sean Owen (JIRA)" <j...@apache.org>
Subject [jira] [Resolved] (SPARK-2335) k-Nearest Neighbor classification and regression for MLLib
Date Sat, 16 Jan 2016 13:39:39 GMT

     [ https://issues.apache.org/jira/browse/SPARK-2335?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Sean Owen resolved SPARK-2335.
------------------------------
    Resolution: Duplicate

Sounds like this is subsumed by discussion of the approximate version

> k-Nearest Neighbor classification and regression for MLLib
> ----------------------------------------------------------
>
>                 Key: SPARK-2335
>                 URL: https://issues.apache.org/jira/browse/SPARK-2335
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>            Reporter: Brian Gawalt
>            Priority: Minor
>              Labels: clustering, features
>
> The k-Nearest Neighbor model for classification and regression problems is a simple and
intuitive approach, offering a straightforward path to creating non-linear decision/estimation
contours. It's downsides -- high variance (sensitivity to the known training data set) and
computational intensity for estimating new point labels -- both play to Spark's big data strengths:
lots of data mitigates data concerns; lots of workers mitigate computational latency. 
> We should include kNN models as options in MLLib.



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