BinaryLogisticRegressionTrainingSummary

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.

   Definition Classes

   BinaryLogisticRegressionSummary

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.

   Definition Classes

   BinaryLogisticRegressionSummary

10. def finalize(): Unit

    

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

    

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12. def hashCode(): Int

    

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13. final def isInstanceOf[T0]: Boolean

    

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14. val labelCol: String

    

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

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

    Definition Classes

    BinaryLogisticRegressionSummary → LogisticRegressionSummary

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

    

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16. final def notify(): Unit

    

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17. final def notifyAll(): Unit

    

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18. val objectiveHistory: Array[Double]

    

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

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

    Definition Classes

    BinaryLogisticRegressionTrainingSummary → LogisticRegressionTrainingSummary

19. lazy val pr: DataFrame

    

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

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

    Definition Classes

    BinaryLogisticRegressionSummary

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.

    Definition Classes

    BinaryLogisticRegressionSummary

21. val predictions: DataFrame

    

    dataframe outputted by the model's transform method.

    dataframe outputted by the model's transform method.

    Definition Classes

    BinaryLogisticRegressionSummary → LogisticRegressionSummary

22. val probabilityCol: String

    

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

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

    Definition Classes

    BinaryLogisticRegressionSummary → LogisticRegressionSummary

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.

    Definition Classes

    BinaryLogisticRegressionSummary

24. lazy val roc: DataFrame

    

    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.

    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.

    Definition Classes

    BinaryLogisticRegressionSummary

    See also

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

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

    

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26. def toString(): String

    

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27. def totalIterations: Int

    

    Number of training iterations until termination

    Number of training iterations until termination

    Definition Classes

    LogisticRegressionTrainingSummary

28. final def wait(): Unit

    

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

    

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

    

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