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t

org.apache.spark.ml.classification

MultilayerPerceptronClassificationSummary

sealed trait MultilayerPerceptronClassificationSummary extends ClassificationSummary

Abstraction for MultilayerPerceptronClassification results for a given model.

Source
MultilayerPerceptronClassifier.scala
Linear Supertypes
ClassificationSummary, Serializable, Serializable, AnyRef, Any
Known Subclasses
MultilayerPerceptronClassificationTrainingSummary
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Inherited
  1. MultilayerPerceptronClassificationSummary
  2. ClassificationSummary
  3. Serializable
  4. Serializable
  5. AnyRef
  6. Any
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Visibility
  1. Public
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Abstract Value Members

  1. abstract def labelCol: String

    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
    ClassificationSummary
    Annotations
    @Since( "3.1.0" )
  2. abstract def predictionCol: String

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

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

    Definition Classes
    ClassificationSummary
    Annotations
    @Since( "3.1.0" )
  3. abstract def predictions: DataFrame

    Dataframe output by the model's transform method.

    Dataframe output by the model's transform method.

    Definition Classes
    ClassificationSummary
    Annotations
    @Since( "3.1.0" )
  4. abstract def weightCol: String

    Field in "predictions" which gives the weight of each instance.

    Field in "predictions" which gives the weight of each instance.

    Definition Classes
    ClassificationSummary
    Annotations
    @Since( "3.1.0" )

Concrete Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. def accuracy: Double

    Returns accuracy.

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

    Definition Classes
    ClassificationSummary
    Annotations
    @Since( "3.1.0" )
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  7. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  8. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  9. def fMeasureByLabel: Array[Double]

    Returns f1-measure for each label (category).

    Returns f1-measure for each label (category).

    Definition Classes
    ClassificationSummary
    Annotations
    @Since( "3.1.0" )
  10. def fMeasureByLabel(beta: Double): Array[Double]

    Returns f-measure for each label (category).

    Returns f-measure for each label (category).

    Definition Classes
    ClassificationSummary
    Annotations
    @Since( "3.1.0" )
  11. def falsePositiveRateByLabel: Array[Double]

    Returns false positive rate for each label (category).

    Returns false positive rate for each label (category).

    Definition Classes
    ClassificationSummary
    Annotations
    @Since( "3.1.0" )
  12. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  13. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  14. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  15. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  16. def labels: Array[Double]

    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
    ClassificationSummary
    Annotations
    @Since( "3.1.0" )
  17. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  18. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  19. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  20. def precisionByLabel: Array[Double]

    Returns precision for each label (category).

    Returns precision for each label (category).

    Definition Classes
    ClassificationSummary
    Annotations
    @Since( "3.1.0" )
  21. def recallByLabel: Array[Double]

    Returns recall for each label (category).

    Returns recall for each label (category).

    Definition Classes
    ClassificationSummary
    Annotations
    @Since( "3.1.0" )
  22. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  23. def toString(): String
    Definition Classes
    AnyRef → Any
  24. def truePositiveRateByLabel: Array[Double]

    Returns true positive rate for each label (category).

    Returns true positive rate for each label (category).

    Definition Classes
    ClassificationSummary
    Annotations
    @Since( "3.1.0" )
  25. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  26. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  27. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  28. def weightedFMeasure: Double

    Returns weighted averaged f1-measure.

    Returns weighted averaged f1-measure.

    Definition Classes
    ClassificationSummary
    Annotations
    @Since( "3.1.0" )
  29. def weightedFMeasure(beta: Double): Double

    Returns weighted averaged f-measure.

    Returns weighted averaged f-measure.

    Definition Classes
    ClassificationSummary
    Annotations
    @Since( "3.1.0" )
  30. def weightedFalsePositiveRate: Double

    Returns weighted false positive rate.

    Returns weighted false positive rate.

    Definition Classes
    ClassificationSummary
    Annotations
    @Since( "3.1.0" )
  31. def weightedPrecision: Double

    Returns weighted averaged precision.

    Returns weighted averaged precision.

    Definition Classes
    ClassificationSummary
    Annotations
    @Since( "3.1.0" )
  32. def weightedRecall: Double

    Returns weighted averaged recall.

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

    Definition Classes
    ClassificationSummary
    Annotations
    @Since( "3.1.0" )
  33. def weightedTruePositiveRate: Double

    Returns weighted true positive rate.

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

    Definition Classes
    ClassificationSummary
    Annotations
    @Since( "3.1.0" )

Inherited from ClassificationSummary

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Members