Trait

org.apache.spark.ml.classification

LogisticRegressionTrainingSummary

Related Doc: package classification

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sealed trait LogisticRegressionTrainingSummary extends LogisticRegressionSummary

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

Annotations
@Experimental()
Source
LogisticRegression.scala
Linear Supertypes
LogisticRegressionSummary, Serializable, Serializable, AnyRef, Any
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  1. LogisticRegressionTrainingSummary
  2. LogisticRegressionSummary
  3. Serializable
  4. Serializable
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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.

    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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    AnyRef → Any
  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. 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 throws an Exception if the summary is not a binary summary.

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

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    Any
  7. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )
  8. final def eq(arg0: AnyRef): Boolean

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

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    Definition Classes
    AnyRef → Any
  10. 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" )
  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).

    Definition Classes
    LogisticRegressionSummary
    Annotations
    @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).

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

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    protected[java.lang]
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    AnyRef
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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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    AnyRef → Any
  16. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  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.

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

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    Definition Classes
    AnyRef
  19. final def notify(): Unit

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    AnyRef
  20. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  21. 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" )
  22. 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" )
  23. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  24. def toString(): String

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    Definition Classes
    AnyRef → Any
  25. def totalIterations: Int

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    Number of training iterations.

    Number of training iterations.

    Annotations
    @Since( "1.5.0" )
  26. 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" )
  27. final def wait(): Unit

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

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

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    AnyRef
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    @throws( ... )
  30. 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" )
  31. 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" )
  32. 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" )
  33. def weightedPrecision: Double

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    Returns weighted averaged precision.

    Returns weighted averaged precision.

    Definition Classes
    LogisticRegressionSummary
    Annotations
    @Since( "2.3.0" )
  34. 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" )
  35. 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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