[jira] [Assigned] (SPARK-13969) Extend input format that feature hashing can handle

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

     [ https://issues.apache.org/jira/browse/SPARK-13969?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-13969:
------------------------------------

    Assignee: Apache Spark

> Extend input format that feature hashing can handle
> ---------------------------------------------------
>
>                 Key: SPARK-13969
>                 URL: https://issues.apache.org/jira/browse/SPARK-13969
>             Project: Spark
>          Issue Type: Sub-task
>          Components: ML, MLlib
>            Reporter: Nick Pentreath
>            Assignee: Apache Spark
>            Priority: Minor
>
> Currently {{HashingTF}} works like {{CountVectorizer}} (the equivalent in scikit-learn is {{HashingVectorizer}}). That is, it works on a sequence of strings and computes term frequencies.
> The use cases for feature hashing extend to arbitrary feature values (binary, count or real-valued). For example, scikit-learn's {{FeatureHasher}} can accept a sequence of (feature_name, value) pairs (e.g. a map, list). In this way, feature hashing can operate as both "one-hot encoder" and "vector assembler" at the same time.
> Investigate adding a more generic feature hasher (that in turn can be used by {{HashingTF}}).



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