Trait

org.apache.spark.ml.classification

LogisticRegressionTrainingSummary

Related Doc: package classification

Permalink

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
Known Subclasses
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. LogisticRegressionTrainingSummary
  2. LogisticRegressionSummary
  3. Serializable
  4. Serializable
  5. AnyRef
  6. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Abstract Value Members

  1. abstract def featuresCol: String

    Permalink

    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

    Permalink

    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]

    Permalink

    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

    Permalink

    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

    Permalink

    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

    Permalink

    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

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

    Permalink
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

    Permalink
    Definition Classes
    AnyRef → Any
  4. def accuracy: Double

    Permalink

    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

    Permalink

    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" )
  6. final def asInstanceOf[T0]: T0

    Permalink
    Definition Classes
    Any
  7. def clone(): AnyRef

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

    Permalink
    Definition Classes
    AnyRef
  9. def equals(arg0: Any): Boolean

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

    Permalink

    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]

    Permalink

    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]

    Permalink

    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

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

    Permalink
    Definition Classes
    AnyRef → Any
  15. def hashCode(): Int

    Permalink
    Definition Classes
    AnyRef → Any
  16. final def isInstanceOf[T0]: Boolean

    Permalink
    Definition Classes
    Any
  17. def labels: Array[Double]

    Permalink

    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

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

    Permalink
    Definition Classes
    AnyRef
  20. final def notifyAll(): Unit

    Permalink
    Definition Classes
    AnyRef
  21. def precisionByLabel: Array[Double]

    Permalink

    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]

    Permalink

    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

    Permalink
    Definition Classes
    AnyRef
  24. def toString(): String

    Permalink
    Definition Classes
    AnyRef → Any
  25. def totalIterations: Int

    Permalink

    Number of training iterations.

    Number of training iterations.

    Annotations
    @Since( "1.5.0" )
  26. def truePositiveRateByLabel: Array[Double]

    Permalink

    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

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

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  29. final def wait(arg0: Long): Unit

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  30. def weightedFMeasure: Double

    Permalink

    Returns weighted averaged f1-measure.

    Returns weighted averaged f1-measure.

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

    Permalink

    Returns weighted averaged f-measure.

    Returns weighted averaged f-measure.

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

    Permalink

    Returns weighted false positive rate.

    Returns weighted false positive rate.

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

    Permalink

    Returns weighted averaged precision.

    Returns weighted averaged precision.

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

    Permalink

    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

    Permalink

    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

Members