Class

org.apache.spark.ml.classification

BinaryLogisticRegressionSummary

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class BinaryLogisticRegressionSummary extends LogisticRegressionSummary

:: Experimental :: Binary Logistic regression results for a given model.

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@Experimental() @Since( "1.5.0" )
Source
LogisticRegression.scala
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LogisticRegressionSummary, Serializable, Serializable, AnyRef, Any
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  1. BinaryLogisticRegressionSummary
  2. LogisticRegressionSummary
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  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

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    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" )
  5. final def asInstanceOf[T0]: T0

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  6. def clone(): AnyRef

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    @throws( ... )
  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

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    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

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    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
    BinaryLogisticRegressionSummaryLogisticRegressionSummary
    Annotations
    @Since( "1.6.0" )
  11. def finalize(): Unit

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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

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

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

    Definition Classes
    BinaryLogisticRegressionSummaryLogisticRegressionSummary
    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

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    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

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    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

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    dataframe output by the model's transform method.

    dataframe output by the model's transform method.

    Definition Classes
    BinaryLogisticRegressionSummaryLogisticRegressionSummary
    Annotations
    @Since( "1.5.0" )
  22. val probabilityCol: String

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    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
    BinaryLogisticRegressionSummaryLogisticRegressionSummary
    Annotations
    @Since( "1.5.0" )
  23. lazy val recallByThreshold: DataFrame

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    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

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    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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Inherited from LogisticRegressionSummary

Inherited from Serializable

Inherited from Serializable

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