Details

    • Type: Sub-task
    • Status: Resolved
    • Priority: Minor
    • Resolution: Fixed
    • Affects Version/s: None
    • Fix Version/s: 2.3.0
    • Component/s: ML, MLlib
    • Labels:
      None

      Description

      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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              • Assignee:
                mlnick Nick Pentreath
                Reporter:
                mlnick Nick Pentreath
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                • Created:
                  Updated:
                  Resolved: