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.

   Annotations

   @Since( "1.5.0" )

   Note

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

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.

    Annotations

    @Since( "1.5.0" )

    Note

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

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 a Dataframe containing two fields recall, precision with (0.

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

    Annotations

    @Since( "1.5.0" )

    Note

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

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.

    Annotations

    @Since( "1.5.0" )

    Note

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

23. val predictions: DataFrame

    dataframe output by the model's transform method.

    dataframe output 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 probability of each class as a vector.

    field in "predictions" which gives the probability of each class as a vector.

    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.

    Annotations

    @Since( "1.5.0" )

    Note

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

26. lazy val roc: DataFrame

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

    Returns the receiver operating characteristic (ROC) curve, which is a Dataframe having two fields (FPR, TPR) with (0.0, 0.0) prepended and (1.0, 1.0) appended to it. See http://en.wikipedia.org/wiki/Receiver_operating_characteristic

    Annotations

    @Since( "1.5.0" )

    Note

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

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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    @throws( ... )