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https://issues.apache.org/jira/browse/SPARK-2335?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14335482#comment-14335482
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Xiangrui Meng commented on SPARK-2335:
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[~Rusty] For exact k-NN, the problem is complexity. So I would prefer starting
from the approximate algorithm directly. Yu's suggestion looks good to me.
Let's discuss more about approximate k-NN to SPARK-2336.
> 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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