org.apache.spark.ml.classification

BinaryLogisticRegressionTrainingSummary

class BinaryLogisticRegressionTrainingSummary extends BinaryLogisticRegressionSummary with LogisticRegressionTrainingSummary

:: Experimental :: Logistic regression training results.

Annotations
@Experimental()
Linear Supertypes
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. BinaryLogisticRegressionTrainingSummary
  2. LogisticRegressionTrainingSummary
  3. BinaryLogisticRegressionSummary
  4. LogisticRegressionSummary
  5. Serializable
  6. Serializable
  7. AnyRef
  8. Any
  1. Hide All
  2. Show all
Learn more about member selection
Visibility
  1. Public
  2. All

Value Members

  1. final def !=(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  2. final def !=(arg0: Any): Boolean

    Definition Classes
    Any
  3. final def ##(): Int

    Definition Classes
    AnyRef → Any
  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  5. final def ==(arg0: Any): Boolean

    Definition Classes
    Any
  6. lazy val areaUnderROC: Double

    Computes the area under the receiver operating characteristic (ROC) curve.

    Computes the area under the receiver operating characteristic (ROC) curve.

    Definition Classes
    BinaryLogisticRegressionSummary
  7. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  8. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  9. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  10. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  11. lazy val fMeasureByThreshold: DataFrame

    Returns a dataframe with two fields (threshold, F-Measure) curve with beta = 1.

    Returns a dataframe with two fields (threshold, F-Measure) curve with beta = 1.0.

    Definition Classes
    BinaryLogisticRegressionSummary
  12. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  13. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  14. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  15. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  16. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  17. final def notify(): Unit

    Definition Classes
    AnyRef
  18. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  19. val objectiveHistory: Array[Double]

    objective function (scaled loss + regularization) at each iteration.

    objective function (scaled loss + regularization) at each iteration.

    Definition Classes
    BinaryLogisticRegressionTrainingSummaryLogisticRegressionTrainingSummary
  20. lazy val pr: DataFrame

    Returns the precision-recall curve, which is an Dataframe containing two fields recall, precision with (0.

    Returns the precision-recall curve, which is an Dataframe containing two fields recall, precision with (0.0, 1.0) prepended to it.

    Definition Classes
    BinaryLogisticRegressionSummary
  21. lazy val precisionByThreshold: DataFrame

    Returns a dataframe with two fields (threshold, precision) curve.

    Returns a dataframe with two fields (threshold, precision) curve. Every possible probability obtained in transforming the dataset are used as thresholds used in calculating the precision.

    Definition Classes
    BinaryLogisticRegressionSummary
  22. lazy val recallByThreshold: DataFrame

    Returns a dataframe with two fields (threshold, recall) curve.

    Returns a dataframe with two fields (threshold, recall) curve. Every possible probability obtained in transforming the dataset are used as thresholds used in calculating the recall.

    Definition Classes
    BinaryLogisticRegressionSummary
  23. lazy val roc: DataFrame

    Returns the receiver operating characteristic (ROC) curve, which is an Dataframe having two fields (FPR, TPR) with (0.

    Returns the receiver operating characteristic (ROC) curve, which is an Dataframe having two fields (FPR, TPR) with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.

    Definition Classes
    BinaryLogisticRegressionSummary
    See also

    http://en.wikipedia.org/wiki/Receiver_operating_characteristic

  24. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  25. def toString(): String

    Definition Classes
    AnyRef → Any
  26. def totalIterations: Int

    Number of training iterations until termination

    Number of training iterations until termination

    Definition Classes
    LogisticRegressionTrainingSummary
  27. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  28. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  29. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from LogisticRegressionSummary

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Members