Trait

org.apache.spark.ml.classification

LogisticRegressionSummary

Related Doc: package classification

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sealed trait LogisticRegressionSummary extends Serializable

:: Experimental :: Abstraction for logistic regression results for a given model.

Currently, the summary ignores the instance weights.

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@Experimental()
Source
LogisticRegression.scala
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Serializable, Serializable, AnyRef, Any
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  1. LogisticRegressionSummary
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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.

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    @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).

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    @Since( "1.5.0" )
  3. 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.

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    @Since( "2.3.0" )
  4. abstract def predictions: DataFrame

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    Dataframe output by the model's transform method.

    Dataframe output by the model's transform method.

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    @Since( "1.5.0" )
  5. 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.

    Annotations
    @Since( "1.5.0" )

Concrete 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. 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.)

    Annotations
    @Since( "2.3.0" )
  5. 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.

    Annotations
    @Since( "2.3.0" )
  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

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

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  9. def equals(arg0: Any): Boolean

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  10. def fMeasureByLabel: Array[Double]

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    Returns f1-measure for each label (category).

    Returns f1-measure for each label (category).

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    @Since( "2.3.0" )
  11. def fMeasureByLabel(beta: Double): Array[Double]

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    Returns f-measure for each label (category).

    Returns f-measure for each label (category).

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    @Since( "2.3.0" )
  12. def falsePositiveRateByLabel: Array[Double]

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    Returns false positive rate for each label (category).

    Returns false positive rate for each label (category).

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    @Since( "2.3.0" )
  13. def finalize(): Unit

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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. 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.

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    @Since( "2.3.0" )
  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. def precisionByLabel: Array[Double]

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    Returns precision for each label (category).

    Returns precision for each label (category).

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    @Since( "2.3.0" )
  22. def recallByLabel: Array[Double]

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    Returns recall for each label (category).

    Returns recall for each label (category).

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    @Since( "2.3.0" )
  23. final def synchronized[T0](arg0: ⇒ T0): T0

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  24. def toString(): String

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  25. def truePositiveRateByLabel: Array[Double]

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    Returns true positive rate for each label (category).

    Returns true positive rate for each label (category).

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    @Since( "2.3.0" )
  26. final def wait(): Unit

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

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

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  29. def weightedFMeasure: Double

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    Returns weighted averaged f1-measure.

    Returns weighted averaged f1-measure.

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    @Since( "2.3.0" )
  30. def weightedFMeasure(beta: Double): Double

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    Returns weighted averaged f-measure.

    Returns weighted averaged f-measure.

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    @Since( "2.3.0" )
  31. def weightedFalsePositiveRate: Double

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    Returns weighted false positive rate.

    Returns weighted false positive rate.

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    @Since( "2.3.0" )
  32. def weightedPrecision: Double

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    Returns weighted averaged precision.

    Returns weighted averaged precision.

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    @Since( "2.3.0" )
  33. def weightedRecall: Double

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    Returns weighted averaged recall.

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

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    @Since( "2.3.0" )
  34. def weightedTruePositiveRate: Double

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    Returns weighted true positive rate.

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

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    @Since( "2.3.0" )

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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