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Posted to issues@spark.apache.org by "Facundo Bellosi (JIRA)" <ji...@apache.org> on 2018/11/26 13:04:00 UTC

[jira] [Created] (SPARK-26173) Prior regularization for Logistic Regression

Facundo Bellosi created SPARK-26173:
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             Summary: Prior regularization for Logistic Regression
                 Key: SPARK-26173
                 URL: https://issues.apache.org/jira/browse/SPARK-26173
             Project: Spark
          Issue Type: New Feature
          Components: MLlib
    Affects Versions: 2.4.0
            Reporter: Facundo Bellosi


This feature enables Maximum A Posteriori (MAP) optimization for Logistic Regression based on a Gaussian prior. In practice, this is just implementing a more general form of L2 regularization parameterized by a (multivariate) mean and precisions vectors. Prior Regularization is enabled when both vectors are provided and regParam > 0 and elasticNetParam < 1.

Reference: Bishop, Christopher M. (2006). _Pattern Recognition and Machine Learning_ (section 4.5). Berlin, Heidelberg: Springer-Verlag.



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