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  1. Spark
  2. SPARK-17645

Add feature selector methods based on: False Discovery Rate (FDR) and Family Wise Error rate (FWE)

    Details

    • Type: New Feature
    • Status: Resolved
    • Priority: Minor
    • Resolution: Fixed
    • Affects Version/s: None
    • Fix Version/s: 2.2.0
    • Component/s: ML, MLlib
    • Labels:
      None
    • Target Version/s:

      Description

      Univariate feature selection works by selecting the best features based on univariate statistical tests.
      FDR and FWE are a popular univariate statistical test for feature selection.

      In 2005, the Benjamini and Hochberg paper on FDR was identified as one of the 25 most-cited statistical papers. The FDR uses the Benjamini-Hochberg procedure in this PR. https://en.wikipedia.org/wiki/False_discovery_rate.
      In statistics, FWE is the probability of making one or more false discoveries, or type I errors, among all the hypotheses when performing multiple hypotheses tests.
      https://en.wikipedia.org/wiki/Family-wise_error_rate

      We add FDR and FWE methods for ChiSqSelector in this PR, like it is implemented in scikit-learn.
      http://scikit-learn.org/stable/modules/feature_selection.html#univariate-feature-selection

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              • Assignee:
                peng.meng@intel.com Peng Meng
                Reporter:
                peng.meng@intel.com Peng Meng
                Shepherd:
                Yanbo Liang
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                  Resolved:

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