keystoneml.evaluation

MulticlassClassifierEvaluator

class MulticlassClassifierEvaluator extends Evaluator[Int, Int, MulticlassMetrics] with Serializable

An evaluator that produces multiclass classification metrics given predicted and actual classes, derived from computing the confusion matrix associated with these two sets of values.

Linear Supertypes
Serializable, Serializable, Evaluator[Int, Int, MulticlassMetrics], AnyRef, Any
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. MulticlassClassifierEvaluator
  2. Serializable
  3. Serializable
  4. Evaluator
  5. AnyRef
  6. Any
  1. Hide All
  2. Show all
Learn more about member selection
Visibility
  1. Public
  2. All

Instance Constructors

  1. new MulticlassClassifierEvaluator(numClasses: Int)

    numClasses

    Number of classes present.

Value Members

  1. final def !=(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  2. final def !=(arg0: Any): Boolean

    Definition Classes
    Any

  3. final def ##(): Int

    Definition Classes
    AnyRef → Any

  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  5. final def ==(arg0: Any): Boolean

    Definition Classes
    Any

  6. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  7. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef

  9. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  10. def evaluate(predictions: RDD[Int], actuals: RDD[Int]): MulticlassMetrics

    Builds the confusion matrix for a multiclass classifier, and provides common metrics such as micro & macro precision & recall

    Builds the confusion matrix for a multiclass classifier, and provides common metrics such as micro & macro precision & recall

    Similar to MLlib's org.apache.spark.mllib.evaluation.MulticlassMetrics, but only does one pass over the data to calculate everything

    predictions

    An RDD of predicted class labels. Must range from 0 until numClasses

    actuals

    An RDD of the true class labels. Must range from 0 until numClasses

    returns

    Common multiclass classifier metrics for this data

    Definition Classes
    MulticlassClassifierEvaluator → Evaluator

  11. def evaluate(predictions: PipelineDataset[Int], labels: PipelineDataset[Int]): MulticlassMetrics

    Definition Classes
    Evaluator

  12. def evaluate(predictions: RDD[Int], labels: PipelineDataset[Int]): MulticlassMetrics

    Definition Classes
    Evaluator

  13. def evaluate(predictions: PipelineDataset[Int], labels: RDD[Int]): MulticlassMetrics

    Definition Classes
    Evaluator

  14. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  15. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any

  16. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  17. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  18. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  19. final def notify(): Unit

    Definition Classes
    AnyRef

  20. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  21. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef

  22. def toString(): String

    Definition Classes
    AnyRef → Any

  23. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from Evaluator[Int, Int, MulticlassMetrics]

Inherited from AnyRef

Inherited from Any

Ungrouped