Trait

org.apache.spark.ml.classification

BinaryLogisticRegressionTrainingSummary

Related Doc: package classification

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sealed trait BinaryLogisticRegressionTrainingSummary extends BinaryLogisticRegressionSummary with LogisticRegressionTrainingSummary

:: Experimental :: Abstraction for binary logistic regression training results. Currently, the training summary ignores the training weights except for the objective trace.

Annotations
@Experimental()
Source
LogisticRegression.scala
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Inherited
  1. BinaryLogisticRegressionTrainingSummary
  2. LogisticRegressionTrainingSummary
  3. BinaryLogisticRegressionSummary
  4. LogisticRegressionSummary
  5. Serializable
  6. Serializable
  7. AnyRef
  8. Any
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Abstract Value Members

  1. abstract def 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
    LogisticRegressionSummary
    Annotations
    @Since( "1.6.0" )
  2. abstract def labelCol: String

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    Field in "predictions" which gives the true label of each instance (if available).

    Field in "predictions" which gives the true label of each instance (if available).

    Definition Classes
    LogisticRegressionSummary
    Annotations
    @Since( "1.5.0" )
  3. abstract def objectiveHistory: Array[Double]

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

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

    Definition Classes
    LogisticRegressionTrainingSummary
    Annotations
    @Since( "1.5.0" )
  4. abstract def predictionCol: String

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    Field in "predictions" which gives the prediction of each class.

    Field in "predictions" which gives the prediction of each class.

    Definition Classes
    LogisticRegressionSummary
    Annotations
    @Since( "2.3.0" )
  5. abstract def predictions: DataFrame

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    Dataframe output by the model's transform method.

    Dataframe output by the model's transform method.

    Definition Classes
    LogisticRegressionSummary
    Annotations
    @Since( "1.5.0" )
  6. abstract def 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
    LogisticRegressionSummary
    Annotations
    @Since( "1.5.0" )

Concrete Value Members

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

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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

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    Definition Classes
    AnyRef → Any
  4. def accuracy: Double

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    Returns accuracy.

    Returns accuracy. (equals to the total number of correctly classified instances out of the total number of instances.)

    Definition Classes
    LogisticRegressionSummary
    Annotations
    @Since( "2.3.0" )
  5. 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.

    Definition Classes
    BinaryLogisticRegressionSummary
    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.

  6. def asBinary: BinaryLogisticRegressionSummary

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    Convenient method for casting to binary logistic regression summary.

    Convenient method for casting to binary logistic regression summary. This method will throw an Exception if the summary is not a binary summary.

    Definition Classes
    LogisticRegressionSummary
    Annotations
    @Since( "2.3.0" )
  7. final def asInstanceOf[T0]: T0

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    Any
  8. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
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    @throws( ... )
  9. final def eq(arg0: AnyRef): Boolean

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    AnyRef
  10. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  11. def fMeasureByLabel: Array[Double]

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    Returns f1-measure for each label (category).

    Returns f1-measure for each label (category).

    Definition Classes
    LogisticRegressionSummary
    Annotations
    @Since( "2.3.0" )
  12. def fMeasureByLabel(beta: Double): Array[Double]

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    Returns f-measure for each label (category).

    Returns f-measure for each label (category).

    Definition Classes
    LogisticRegressionSummary
    Annotations
    @Since( "2.3.0" )
  13. 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.

    Definition Classes
    BinaryLogisticRegressionSummary
    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.

  14. def falsePositiveRateByLabel: Array[Double]

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    Returns false positive rate for each label (category).

    Returns false positive rate for each label (category).

    Definition Classes
    LogisticRegressionSummary
    Annotations
    @Since( "2.3.0" )
  15. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
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    Annotations
    @throws( classOf[java.lang.Throwable] )
  16. final def getClass(): Class[_]

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  17. def hashCode(): Int

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  18. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  19. def labels: Array[Double]

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    Returns the sequence of labels in ascending order.

    Returns the sequence of labels in ascending order. This order matches the order used in metrics which are specified as arrays over labels, e.g., truePositiveRateByLabel.

    Note: In most cases, it will be values {0.0, 1.0, ..., numClasses-1}, However, if the training set is missing a label, then all of the arrays over labels (e.g., from truePositiveRateByLabel) will be of length numClasses-1 instead of the expected numClasses.

    Definition Classes
    LogisticRegressionSummary
    Annotations
    @Since( "2.3.0" )
  20. final def ne(arg0: AnyRef): Boolean

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  21. final def notify(): Unit

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    Definition Classes
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  22. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  23. 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.

    Definition Classes
    BinaryLogisticRegressionSummary
    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.

  24. def precisionByLabel: Array[Double]

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    Returns precision for each label (category).

    Returns precision for each label (category).

    Definition Classes
    LogisticRegressionSummary
    Annotations
    @Since( "2.3.0" )
  25. 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.

    Definition Classes
    BinaryLogisticRegressionSummary
    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. def recallByLabel: Array[Double]

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    Returns recall for each label (category).

    Returns recall for each label (category).

    Definition Classes
    LogisticRegressionSummary
    Annotations
    @Since( "2.3.0" )
  27. 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.

    Definition Classes
    BinaryLogisticRegressionSummary
    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.

  28. 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

    Definition Classes
    BinaryLogisticRegressionSummary
    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.

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

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    AnyRef
  30. def toString(): String

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    AnyRef → Any
  31. def totalIterations: Int

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    Number of training iterations.

    Number of training iterations.

    Definition Classes
    LogisticRegressionTrainingSummary
    Annotations
    @Since( "1.5.0" )
  32. def truePositiveRateByLabel: Array[Double]

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    Returns true positive rate for each label (category).

    Returns true positive rate for each label (category).

    Definition Classes
    LogisticRegressionSummary
    Annotations
    @Since( "2.3.0" )
  33. final def wait(): Unit

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    @throws( ... )
  34. final def wait(arg0: Long, arg1: Int): Unit

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    @throws( ... )
  35. final def wait(arg0: Long): Unit

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    @throws( ... )
  36. def weightedFMeasure: Double

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    Returns weighted averaged f1-measure.

    Returns weighted averaged f1-measure.

    Definition Classes
    LogisticRegressionSummary
    Annotations
    @Since( "2.3.0" )
  37. def weightedFMeasure(beta: Double): Double

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    Returns weighted averaged f-measure.

    Returns weighted averaged f-measure.

    Definition Classes
    LogisticRegressionSummary
    Annotations
    @Since( "2.3.0" )
  38. def weightedFalsePositiveRate: Double

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    Returns weighted false positive rate.

    Returns weighted false positive rate.

    Definition Classes
    LogisticRegressionSummary
    Annotations
    @Since( "2.3.0" )
  39. def weightedPrecision: Double

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    Returns weighted averaged precision.

    Returns weighted averaged precision.

    Definition Classes
    LogisticRegressionSummary
    Annotations
    @Since( "2.3.0" )
  40. def weightedRecall: Double

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    Returns weighted averaged recall.

    Returns weighted averaged recall. (equals to precision, recall and f-measure)

    Definition Classes
    LogisticRegressionSummary
    Annotations
    @Since( "2.3.0" )
  41. def weightedTruePositiveRate: Double

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    Returns weighted true positive rate.

    Returns weighted true positive rate. (equals to precision, recall and f-measure)

    Definition Classes
    LogisticRegressionSummary
    Annotations
    @Since( "2.3.0" )

Inherited from LogisticRegressionSummary

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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