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

    https://github.com/apache/spark/pull/15435#discussion_r94616573
  
    --- Diff: 
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
 ---
    @@ -1120,21 +1239,129 @@ sealed trait LogisticRegressionSummary extends 
Serializable {
       /** Field in "predictions" which gives the probability of each class as 
a vector. */
       def probabilityCol: String
     
    +  /** Field in "predictions" which gives the prediction of each class as a 
vector. */
    +  def predictionCol: String
    +
       /** Field in "predictions" which gives the true label of each instance 
(if available). */
       def labelCol: String
     
       /** Field in "predictions" which gives the features of each instance as 
a vector. */
       def featuresCol: String
     
    +  /** Returns false positive rate for each label. */
    +  @Since("2.1.0")
    +  def falsePositiveRateByLabel: Array[Double]
    +
    +  /** Returns precision for each label. */
    +  @Since("2.1.0")
    +  def precisionByLabel: Array[Double]
    +
    +  /** Returns recall for each label. */
    +  @Since("2.1.0")
    +  def recallByLabel: Array[Double]
    +
    +  /**
    +   * Returns f-measure for each label.
    +   * @param beta the beta parameter.
    +   */
    +  @Since("2.1.0")
    +  def fMeasureByLabel(beta: Double): Array[Double]
    +
    +  /** Returns f1-measure for each label. */
    +  @Since("2.1.0")
    +  def fMeasureByLabel: Array[Double]
    +
    +  /** Returns accuracy. */
    +  @Since("2.1.0")
    +  def accuracy: Double
    +
    +  /** Returns weighted false positive rate. */
    +  @Since("2.1.0")
    +  def weightedFalsePositiveRate: Double
    +
    +  /** Returns weighted averaged recall. */
    +  @Since("2.1.0")
    +  def weightedRecall: Double
    +
    +  /** Returns weighted averaged precision. */
    +  @Since("2.1.0")
    +  def weightedPrecision: Double
    +
    +  /**
    +   * Returns weighted averaged f-measure.
    +   * @param beta the beta parameter.
    +   */
    +  @Since("2.1.0")
    +  def weightedFMeasure(beta: Double): Double
    +
    +  /** Returns weighted averaged f1-measure. */
    +  @Since("2.1.0")
    +  def weightedFMeasure: Double
     }
     
     /**
      * :: Experimental ::
    - * Logistic regression training results.
    + * Multinomial Logistic regression training results.
    --- End diff --
    
    nit: don't capitalize logistic here and elsewhere


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