BinaryLogisticRegressionSummary

Value Members

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

   

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2. final def ##(): Int

   

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

   

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4. 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.

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   @Since( "1.5.0" )

5. final def asInstanceOf[T0]: T0

   

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6. def clone(): AnyRef

   

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

   

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8. def equals(arg0: Any): Boolean

   

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9. lazy val fMeasureByThreshold: DataFrame

   

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

   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" )

10. 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" )

11. def finalize(): Unit

    

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    protected[java.lang]

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

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

    

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13. def hashCode(): Int

    

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14. final def isInstanceOf[T0]: Boolean

    

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15. 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" )

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. lazy val pr: DataFrame

    

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

    Returns the precision-recall curve, which is a 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" )

20. 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" )

21. 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" )

22. 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" )

23. 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" )

24. lazy val roc: DataFrame

    

    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.

    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

    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" )

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

    

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26. def toString(): String

    

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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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