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  1. Spark
  2. SPARK-8521 Feature Transformers in 1.5
  3. SPARK-7514

Add MinMaxScaler to feature transformation

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    Details

    • Type: Sub-task
    • Status: Resolved
    • Priority: Major
    • Resolution: Fixed
    • Affects Version/s: None
    • Fix Version/s: 1.5.0
    • Component/s: MLlib
    • Labels:
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    • Target Version/s:

      Description

      Add a popular scaling method to feature component, which is commonly known as min-max normalization or Rescaling.

      Core function is,
      Normalized( x ) = (x - min) / (max - min) * scale + newBase

      where newBase and scale are parameters of the VectorTransformer. newBase is the new minimum number for the feature, and scale controls the range after transformation. This is a little complicated than the basic MinMax normalization, yet it provides flexibility so that users can control the range more specifically. like [0.1, 0.9] in some NN application.

      for case that max == min, 0.5 is used as the raw value.

      reference:
      http://en.wikipedia.org/wiki/Feature_scaling
      http://stn.spotfire.com/spotfire_client_help/index.htm#norm/norm_scale_between_0_and_1.htm

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              • Assignee:
                yuhaoyan yuhao yang
                Reporter:
                yuhaoyan yuhao yang
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