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t

org.apache.spark.ml.classification

BinaryLogisticRegressionTrainingSummary

sealed trait BinaryLogisticRegressionTrainingSummary extends BinaryLogisticRegressionSummary with LogisticRegressionTrainingSummary

Abstraction for binary logistic regression training results.

Linear Supertypes
LogisticRegressionTrainingSummary, TrainingSummary, BinaryLogisticRegressionSummary, BinaryClassificationSummary, LogisticRegressionSummary, ClassificationSummary, Serializable, Serializable, AnyRef, Any
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Inherited
  1. BinaryLogisticRegressionTrainingSummary
  2. LogisticRegressionTrainingSummary
  3. TrainingSummary
  4. BinaryLogisticRegressionSummary
  5. BinaryClassificationSummary
  6. LogisticRegressionSummary
  7. ClassificationSummary
  8. Serializable
  9. Serializable
  10. AnyRef
  11. Any
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Abstract Value Members

  1. abstract def featuresCol: String

    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

    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" )
  3. abstract def objectiveHistory: Array[Double]

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

    objective function (scaled loss + regularization) at each iteration. It contains one more element, the initial state, than number of iterations.

    Definition Classes
    TrainingSummary
    Annotations
    @Since( "3.1.0" )
  4. 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" )
  5. 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" )
  6. abstract def probabilityCol: String

    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" )
  7. 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. lazy val areaUnderROC: Double

    Computes the area under the receiver operating characteristic (ROC) curve.

    Computes the area under the receiver operating characteristic (ROC) curve.

    Definition Classes
    BinaryClassificationSummary
    Annotations
    @Since( "3.1.0" )
  6. def asBinary: BinaryLogisticRegressionSummary

    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
    Definition Classes
    Any
  8. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  9. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  10. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  11. 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" )
  12. 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" )
  13. lazy val fMeasureByThreshold: DataFrame

    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
    BinaryClassificationSummary
    Annotations
    @Since( "3.1.0" ) @transient()
  14. 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" )
  15. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  16. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  17. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  18. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  19. 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" )
  20. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  21. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  22. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  23. lazy val pr: DataFrame

    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
    BinaryClassificationSummary
    Annotations
    @Since( "3.1.0" ) @transient()
  24. def precisionByLabel: Array[Double]

    Returns precision for each label (category).

    Returns precision for each label (category).

    Definition Classes
    ClassificationSummary
    Annotations
    @Since( "3.1.0" )
  25. lazy val precisionByThreshold: DataFrame

    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
    BinaryClassificationSummary
    Annotations
    @Since( "3.1.0" ) @transient()
  26. def recallByLabel: Array[Double]

    Returns recall for each label (category).

    Returns recall for each label (category).

    Definition Classes
    ClassificationSummary
    Annotations
    @Since( "3.1.0" )
  27. lazy val recallByThreshold: DataFrame

    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
    BinaryClassificationSummary
    Annotations
    @Since( "3.1.0" ) @transient()
  28. lazy val roc: DataFrame

    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
    BinaryClassificationSummary
    Annotations
    @Since( "3.1.0" ) @transient()
  29. def scoreCol: String

    Field in "predictions" which gives the probability or rawPrediction of each class as a vector.

    Field in "predictions" which gives the probability or rawPrediction of each class as a vector.

    Definition Classes
    BinaryLogisticRegressionSummary → BinaryClassificationSummary
  30. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  31. def toString(): String
    Definition Classes
    AnyRef → Any
  32. def totalIterations: Int

    Number of training iterations.

    Number of training iterations.

    Definition Classes
    TrainingSummary
    Annotations
    @Since( "3.1.0" )
  33. 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" )
  34. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  35. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  36. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  37. def weightedFMeasure: Double

    Returns weighted averaged f1-measure.

    Returns weighted averaged f1-measure.

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

    Returns weighted averaged f-measure.

    Returns weighted averaged f-measure.

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

    Returns weighted false positive rate.

    Returns weighted false positive rate.

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

    Returns weighted averaged precision.

    Returns weighted averaged precision.

    Definition Classes
    ClassificationSummary
    Annotations
    @Since( "3.1.0" )
  41. 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" )
  42. 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 LogisticRegressionTrainingSummary

Inherited from TrainingSummary

Inherited from BinaryLogisticRegressionSummary

Inherited from BinaryClassificationSummary

Inherited from LogisticRegressionSummary

Inherited from ClassificationSummary

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Members