[jira] [Commented] (SPARK-13097) Extend Binarizer to allow Double AND Vector inputs

["Apache Spark (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-13097?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15124323#comment-15124323 ] 

Apache Spark commented on SPARK-13097:
--------------------------------------

User 'seddonm1' has created a pull request for this issue:
https://github.com/apache/spark/pull/10976

> Extend Binarizer to allow Double AND Vector inputs
> --------------------------------------------------
>
>                 Key: SPARK-13097
>                 URL: https://issues.apache.org/jira/browse/SPARK-13097
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML
>            Reporter: Mike Seddon
>            Priority: Minor
>
> To enhance the existing SparkML Binarizer [SPARK-5891] to allow Vector in addition to the existing Double input column type.
> A use case for this enhancement is for when a user wants to Binarize many similar feature columns at once using the same threshold value.
> A real-world example for this would be where the authors of one of the leading MNIST handwriting character recognition entries converts 784 grayscale (0-255) pixels (28x28 pixel images) to binary if the pixel's grayscale exceeds 127.5: (http://arxiv.org/abs/1003.0358). With this modification the user is able to: VectorAssembler(784 columns)->Binarizer(127.5)->Classifier as all the pixels are of a similar type. 
> This approach also allows much easier use of the ParamGridBuilder to test multiple theshold values.
> I have already written the code and unit tests and have tested in a Multilayer perceptron classifier workflow.



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