Class/Object

org.apache.spark.ml.evaluation

BinaryClassificationEvaluator

Related Docs: object BinaryClassificationEvaluator | package evaluation

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class BinaryClassificationEvaluator extends Evaluator with HasRawPredictionCol with HasLabelCol with DefaultParamsWritable

:: Experimental :: Evaluator for binary classification, which expects two input columns: rawPrediction and label. The rawPrediction column can be of type double (binary 0/1 prediction, or probability of label 1) or of type vector (length-2 vector of raw predictions, scores, or label probabilities).

Annotations
@Since( "1.2.0" ) @Experimental()
Source
BinaryClassificationEvaluator.scala
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Inherited
  1. BinaryClassificationEvaluator
  2. DefaultParamsWritable
  3. MLWritable
  4. HasLabelCol
  5. HasRawPredictionCol
  6. Evaluator
  7. Params
  8. Serializable
  9. Serializable
  10. Identifiable
  11. AnyRef
  12. Any
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Visibility
  1. Public
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Instance Constructors

  1. new BinaryClassificationEvaluator()

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    Annotations
    @Since( "1.2.0" )
  2. new BinaryClassificationEvaluator(uid: String)

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    Annotations
    @Since( "1.4.0" )

Value Members

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

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    Definition Classes
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  2. final def ##(): Int

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  3. final def $[T](param: Param[T]): T

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    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean

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  5. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  6. final def clear(param: Param[_]): BinaryClassificationEvaluator.this.type

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    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  7. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
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    @throws( ... )
  8. def copy(extra: ParamMap): BinaryClassificationEvaluator

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    Creates a copy of this instance with the same UID and some extra params.

    Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

    Definition Classes
    BinaryClassificationEvaluatorEvaluatorParams
    Annotations
    @Since( "1.4.1" )
  9. def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

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    Copies param values from this instance to another instance for params shared by them.

    Copies param values from this instance to another instance for params shared by them.

    This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.

    to

    the target instance, which should work with the same set of default Params as this source instance

    extra

    extra params to be copied to the target's paramMap

    returns

    the target instance with param values copied

    Attributes
    protected
    Definition Classes
    Params
  10. final def defaultCopy[T <: Params](extra: ParamMap): T

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    Default implementation of copy with extra params.

    Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

    Attributes
    protected
    Definition Classes
    Params
  11. final def eq(arg0: AnyRef): Boolean

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

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  13. def evaluate(dataset: Dataset[_]): Double

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    Evaluates model output and returns a scalar metric.

    Evaluates model output and returns a scalar metric. The value of isLargerBetter specifies whether larger values are better.

    dataset

    a dataset that contains labels/observations and predictions.

    returns

    metric

    Definition Classes
    BinaryClassificationEvaluatorEvaluator
    Annotations
    @Since( "2.0.0" )
  14. def evaluate(dataset: Dataset[_], paramMap: ParamMap): Double

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    Evaluates model output and returns a scalar metric.

    Evaluates model output and returns a scalar metric. The value of isLargerBetter specifies whether larger values are better.

    dataset

    a dataset that contains labels/observations and predictions.

    paramMap

    parameter map that specifies the input columns and output metrics

    returns

    metric

    Definition Classes
    Evaluator
    Annotations
    @Since( "2.0.0" )
  15. def explainParam(param: Param[_]): String

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    Explains a param.

    Explains a param.

    param

    input param, must belong to this instance.

    returns

    a string that contains the input param name, doc, and optionally its default value and the user-supplied value

    Definition Classes
    Params
  16. def explainParams(): String

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    Explains all params of this instance.

    Explains all params of this instance. See explainParam().

    Definition Classes
    Params
  17. final def extractParamMap(): ParamMap

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    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  18. final def extractParamMap(extra: ParamMap): ParamMap

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    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Definition Classes
    Params
  19. def finalize(): Unit

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    protected[java.lang]
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    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  20. final def get[T](param: Param[T]): Option[T]

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    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes
    Params
  21. final def getClass(): Class[_]

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  22. final def getDefault[T](param: Param[T]): Option[T]

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    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  23. final def getLabelCol: String

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    Definition Classes
    HasLabelCol
  24. def getMetricName: String

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    Annotations
    @Since( "1.2.0" )
  25. final def getOrDefault[T](param: Param[T]): T

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    Gets the value of a param in the embedded param map or its default value.

    Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.

    Definition Classes
    Params
  26. def getParam(paramName: String): Param[Any]

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    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  27. final def getRawPredictionCol: String

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    Definition Classes
    HasRawPredictionCol
  28. final def hasDefault[T](param: Param[T]): Boolean

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    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

    Definition Classes
    Params
  29. def hasParam(paramName: String): Boolean

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    Tests whether this instance contains a param with a given name.

    Tests whether this instance contains a param with a given name.

    Definition Classes
    Params
  30. def hashCode(): Int

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  31. final def isDefined(param: Param[_]): Boolean

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    Checks whether a param is explicitly set or has a default value.

    Checks whether a param is explicitly set or has a default value.

    Definition Classes
    Params
  32. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  33. def isLargerBetter: Boolean

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    Indicates whether the metric returned by evaluate should be maximized (true, default) or minimized (false).

    Indicates whether the metric returned by evaluate should be maximized (true, default) or minimized (false). A given evaluator may support multiple metrics which may be maximized or minimized.

    Definition Classes
    BinaryClassificationEvaluatorEvaluator
    Annotations
    @Since( "1.5.0" )
  34. final def isSet(param: Param[_]): Boolean

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    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  35. final val labelCol: Param[String]

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    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  36. val metricName: Param[String]

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    param for metric name in evaluation (supports "areaUnderROC" (default), "areaUnderPR")

    param for metric name in evaluation (supports "areaUnderROC" (default), "areaUnderPR")

    Annotations
    @Since( "1.2.0" )
  37. final def ne(arg0: AnyRef): Boolean

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  38. final def notify(): Unit

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  39. final def notifyAll(): Unit

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  40. lazy val params: Array[Param[_]]

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    Returns all params sorted by their names.

    Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.

    Definition Classes
    Params
    Note

    Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.

  41. final val rawPredictionCol: Param[String]

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    Param for raw prediction (a.k.a.

    Param for raw prediction (a.k.a. confidence) column name.

    Definition Classes
    HasRawPredictionCol
  42. def save(path: String): Unit

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    Saves this ML instance to the input path, a shortcut of write.save(path).

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  43. final def set(paramPair: ParamPair[_]): BinaryClassificationEvaluator.this.type

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    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  44. final def set(param: String, value: Any): BinaryClassificationEvaluator.this.type

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    Sets a parameter (by name) in the embedded param map.

    Sets a parameter (by name) in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  45. final def set[T](param: Param[T], value: T): BinaryClassificationEvaluator.this.type

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    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  46. final def setDefault(paramPairs: ParamPair[_]*): BinaryClassificationEvaluator.this.type

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    Sets default values for a list of params.

    Sets default values for a list of params.

    Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.

    paramPairs

    a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.

    Attributes
    protected
    Definition Classes
    Params
  47. final def setDefault[T](param: Param[T], value: T): BinaryClassificationEvaluator.this.type

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    Sets a default value for a param.

    Sets a default value for a param.

    param

    param to set the default value. Make sure that this param is initialized before this method gets called.

    value

    the default value

    Attributes
    protected
    Definition Classes
    Params
  48. def setLabelCol(value: String): BinaryClassificationEvaluator.this.type

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    Annotations
    @Since( "1.2.0" )
  49. def setMetricName(value: String): BinaryClassificationEvaluator.this.type

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    Annotations
    @Since( "1.2.0" )
  50. def setRawPredictionCol(value: String): BinaryClassificationEvaluator.this.type

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    Annotations
    @Since( "1.5.0" )
  51. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  52. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any
  53. val uid: String

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    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    BinaryClassificationEvaluatorIdentifiable
    Annotations
    @Since( "1.4.0" )
  54. final def wait(): Unit

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

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

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    @throws( ... )
  57. def write: MLWriter

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    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DefaultParamsWritableMLWritable

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from HasLabelCol

Inherited from HasRawPredictionCol

Inherited from Evaluator

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

Members

Parameter setters

Parameter getters