Peng Meng created SPARK-17017:
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Summary: Add a chiSquare Selector based on False Positive Rate
(FPR) test
Key: SPARK-17017
URL: https://issues.apache.org/jira/browse/SPARK-17017
Project: Spark
Issue Type: New Feature
Affects Versions: 2.0.0
Reporter: Peng Meng
Priority: Minor
Univariate feature selection works by selecting the best features based on
univariate statistical tests. False Positive Rate (FPR) is a popular univariate
statistical test for feature selection. Is it necessary to add a chiSquare
Selector based on False Positive Rate (FPR) test, like it is implemented in
scikit-learn.
http://scikit-learn.org/stable/modules/feature_selection.html#univariate-feature-selection
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