BinaryLogisticRegressionTrainingSummary

Value Members

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

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

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3. final def ##(): Int

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4. final def ==(arg0: AnyRef): Boolean

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5. final def ==(arg0: Any): Boolean

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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.

   Note: This ignores instance weights (setting all to 1.0) from LogisticRegression.weightCol. This will change in later Spark versions.

   Definition Classes

   BinaryLogisticRegressionSummary

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   @Since( "1.5.0" )

7. final def asInstanceOf[T0]: T0

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8. def clone(): AnyRef

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   protected[java.lang]

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9. final def eq(arg0: AnyRef): Boolean

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10. def equals(arg0: Any): Boolean

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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.

    Note: This ignores instance weights (setting all to 1.0) from LogisticRegression.weightCol. This will change in later Spark versions.

    Definition Classes

    BinaryLogisticRegressionSummary

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    @Since( "1.5.0" )

12. def finalize(): Unit

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    protected[java.lang]

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    @throws( classOf[java.lang.Throwable] )

13. final def getClass(): Class[_]

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14. def hashCode(): Int

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15. final def isInstanceOf[T0]: Boolean

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16. final def ne(arg0: AnyRef): Boolean

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17. final def notify(): Unit

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18. final def notifyAll(): Unit

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19. val objectiveHistory: Array[Double]

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

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

    Definition Classes

    BinaryLogisticRegressionTrainingSummary → LogisticRegressionTrainingSummary

    Annotations

    @Since( "1.5.0" )

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.

    Note: This ignores instance weights (setting all to 1.0) from LogisticRegression.weightCol. This will change in later Spark versions.

    Definition Classes

    BinaryLogisticRegressionSummary

    Annotations

    @Since( "1.5.0" )

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.

    Note: This ignores instance weights (setting all to 1.0) from LogisticRegression.weightCol. This will change in later Spark versions.

    Definition Classes

    BinaryLogisticRegressionSummary

    Annotations

    @Since( "1.5.0" )

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.

    Note: This ignores instance weights (setting all to 1.0) from LogisticRegression.weightCol. This will change in later Spark versions.

    Definition Classes

    BinaryLogisticRegressionSummary

    Annotations

    @Since( "1.5.0" )

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.

    Note: This ignores instance weights (setting all to 1.0) from LogisticRegression.weightCol. This will change in later Spark versions.

    Definition Classes

    BinaryLogisticRegressionSummary

    Annotations

    @Since( "1.5.0" )

    See also

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

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

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25. def toString(): String

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26. def totalIterations: Int

    Number of training iterations until termination

    Number of training iterations until termination

    Definition Classes

    LogisticRegressionTrainingSummary

27. final def wait(): Unit

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28. final def wait(arg0: Long, arg1: Int): Unit

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29. final def wait(arg0: Long): Unit

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    @throws( ... )