Github user mpjlu commented on a diff in the pull request:

    https://github.com/apache/spark/pull/14597#discussion_r79405860
  
    --- Diff: 
mllib/src/main/scala/org/apache/spark/ml/feature/ChiSqSelector.scala ---
    @@ -54,11 +55,44 @@ private[feature] trait ChiSqSelectorParams extends 
Params
     
       /** @group getParam */
       def getNumTopFeatures: Int = $(numTopFeatures)
    +
    +  final val percentile = new DoubleParam(this, "percentile",
    +    "Percentile of features that selector will select, ordered by 
statistics value descending.",
    +    ParamValidators.inRange(0, 1))
    +  setDefault(percentile -> 0.1)
    +
    +  /** @group getParam */
    +  def getPercentile: Double = $(percentile)
    +
    +  final val alpha = new DoubleParam(this, "alpha",
    +    "The highest p-value for features to be kept.",
    +    ParamValidators.inRange(0, 1))
    +  setDefault(alpha -> 0.05)
    +
    +  /** @group getParam */
    +  def getAlpha: Double = $(alpha)
    +
    +  /**
    +   * The ChiSqSelector supports KBest, Percentile, FPR selection,
    +   * which is the same as ChiSqSelectorType defined in MLLIB.
    +   * when call setNumTopFeatures, the selectorType is set to KBest
    +   * when call setPercentile, the selectorType is set to Percentile
    +   * when call setAlpha, the selectorType is set to FPR
    +   */
    +  final val selectorType = new Param[String](this, "selectorType",
    +    "ChiSqSelector Type: KBest, Percentile, FPR")
    +  setDefault(selectorType -> ChiSqSelectorType.KBest.toString)
    +
    +  /** @group getParam */
    +  def getChiSqSelectorType: String = $(selectorType)
     }
     
     /**
      * Chi-Squared feature selection, which selects categorical features to 
use for predicting a
      * categorical label.
    + * The selector supports three selection methods: KBest, Percentile and 
FPR.
    --- End diff --
    
     Thanks @srowen, I have updated the javadoc. 


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