[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.

1 files changed, 3 insertions, 0 deletions

diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index d4cbf510b9..f13f3ff789 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -815,6 +815,9 @@ object MimaExcludes {
     ) ++ Seq(
       // [SPARK-17163] Unify logistic regression interface. Private constructor has new signature.
       ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionModel.this")
+    ) ++ Seq(
+      // [SPARK-17017] Add chiSquare selector based on False Positive Rate (FPR) test
+      ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.mllib.feature.ChiSqSelectorModel.isSorted")
     )
   }