Peng, Meng b366f18496 [SPARK-17017][MLLIB][ML] add a chiSquare Selector based on False Positive Rate (FPR) test ... ## What changes were proposed in this pull request? 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. We add a chiSquare Selector based on False Positive Rate (FPR) test in this PR, like it is implemented in scikit-learn. http://scikit-learn.org/stable/modules/feature_selection.html#univariate-feature-selection ## How was this patch tested? Add Scala ut Author: Peng, Meng <peng.meng@intel.com> Closes #14597 from mpjlu/fprChiSquare.		2016-09-21 10:17:38 +01:00
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build.properties	[SPARK-13834][BUILD] Update sbt and sbt plugins for 2.x.	2016-03-13 18:47:04 -07:00
MimaBuild.scala	[SPARK-14818] Post-2.0 MiMa exclusion and build changes	2016-09-12 15:24:33 -07:00
MimaExcludes.scala	[SPARK-17017][MLLIB][ML] add a chiSquare Selector based on False Positive Rate (FPR) test	2016-09-21 10:17:38 +01:00
plugins.sbt	[SPARK-15827][BUILD] Publish Spark's forked sbt-pom-reader to Maven Central	2016-06-09 11:04:08 -07:00
SparkBuild.scala	[SPARK-14818] Post-2.0 MiMa exclusion and build changes	2016-09-12 15:24:33 -07:00