BinaryLogisticRegressionSummary

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.

   Annotations

   @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.

    Annotations

    @Since( "1.5.0" )

12. val featuresCol: String

    field in "predictions" which gives the features of each instance as a vector.

    field in "predictions" which gives the features of each instance as a vector.

    Definition Classes

    BinaryLogisticRegressionSummary → LogisticRegressionSummary

    Annotations

    @Since( "1.6.0" )

13. def finalize(): Unit

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    protected[java.lang]

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

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

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15. def hashCode(): Int

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16. final def isInstanceOf[T0]: Boolean

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17. val labelCol: String

    field in "predictions" which gives the true label of each instance.

    field in "predictions" which gives the true label of each instance.

    Definition Classes

    BinaryLogisticRegressionSummary → LogisticRegressionSummary

    Annotations

    @Since( "1.5.0" )

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

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19. final def notify(): Unit

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20. final def notifyAll(): Unit

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21. 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.

    Annotations

    @Since( "1.5.0" )

22. 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.

    Annotations

    @Since( "1.5.0" )

23. val predictions: DataFrame

    dataframe outputted by the model's transform method.

    dataframe outputted by the model's transform method.

    Definition Classes

    BinaryLogisticRegressionSummary → LogisticRegressionSummary

    Annotations

    @Since( "1.5.0" )

24. val probabilityCol: String

    field in "predictions" which gives the calibrated probability of each instance.

    field in "predictions" which gives the calibrated probability of each instance.

    Definition Classes

    BinaryLogisticRegressionSummary → LogisticRegressionSummary

    Annotations

    @Since( "1.5.0" )

25. 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.

    Annotations

    @Since( "1.5.0" )

26. 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.

    Annotations

    @Since( "1.5.0" )

    See also

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

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

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28. def toString(): String

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29. final def wait(): Unit

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

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

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