Packages

class BinaryClassificationEvaluator extends Evaluator with HasRawPredictionCol with HasLabelCol with HasWeightCol with DefaultParamsWritable

Evaluator for binary classification, which expects input columns rawPrediction, label and an optional weight column. 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" )
Source
BinaryClassificationEvaluator.scala
Linear Supertypes
DefaultParamsWritable, MLWritable, HasWeightCol, HasLabelCol, HasRawPredictionCol, Evaluator, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. BinaryClassificationEvaluator
  2. DefaultParamsWritable
  3. MLWritable
  4. HasWeightCol
  5. HasLabelCol
  6. HasRawPredictionCol
  7. Evaluator
  8. Params
  9. Serializable
  10. Serializable
  11. Identifiable
  12. AnyRef
  13. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new BinaryClassificationEvaluator()
    Annotations
    @Since( "1.2.0" )
  2. new BinaryClassificationEvaluator(uid: String)
    Annotations
    @Since( "1.4.0" )

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. final def clear(param: Param[_]): BinaryClassificationEvaluator.this.type

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

    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
    BinaryClassificationEvaluator → Evaluator → Params
    Annotations
    @Since( "1.4.1" )
  9. def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

    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

    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
    Definition Classes
    AnyRef
  12. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  13. def evaluate(dataset: Dataset[_]): Double

    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
    BinaryClassificationEvaluator → Evaluator
    Annotations
    @Since( "2.0.0" )
  14. def evaluate(dataset: Dataset[_], paramMap: ParamMap): Double

    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

    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

    Explains all params of this instance.

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

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

    extractParamMap with no extra values.

    extractParamMap with no extra values.

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

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

    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes
    Params
  20. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @IntrinsicCandidate()
  21. final def getDefault[T](param: Param[T]): Option[T]

    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  22. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  23. def getMetricName: String

    Annotations
    @Since( "1.2.0" )
  24. def getMetrics(dataset: Dataset[_]): BinaryClassificationMetrics

    Get a BinaryClassificationMetrics, which can be used to get binary classification metrics such as areaUnderROC and areaUnderPR.

    Get a BinaryClassificationMetrics, which can be used to get binary classification metrics such as areaUnderROC and areaUnderPR.

    dataset

    a dataset that contains labels/observations and predictions.

    returns

    BinaryClassificationMetrics

    Annotations
    @Since( "3.1.0" )
  25. def getNumBins: Int

    Annotations
    @Since( "3.0.0" )
  26. final def getOrDefault[T](param: Param[T]): T

    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
  27. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  28. final def getRawPredictionCol: String

    Definition Classes
    HasRawPredictionCol
  29. final def getWeightCol: String

    Definition Classes
    HasWeightCol
  30. final def hasDefault[T](param: Param[T]): Boolean

    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

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

    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
  32. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @IntrinsicCandidate()
  33. final def isDefined(param: Param[_]): Boolean

    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
  34. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  35. def isLargerBetter: Boolean

    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
    BinaryClassificationEvaluator → Evaluator
    Annotations
    @Since( "1.5.0" )
  36. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

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

    Param for label column name.

    Param for label column name.

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

    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" )
  39. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  40. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @IntrinsicCandidate()
  41. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @IntrinsicCandidate()
  42. val numBins: IntParam

    param for number of bins to down-sample the curves (ROC curve, PR curve) in area computation.

    param for number of bins to down-sample the curves (ROC curve, PR curve) in area computation. If 0, no down-sampling will occur. Default: 1000.

    Annotations
    @Since( "3.0.0" )
  43. lazy val params: Array[Param[_]]

    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.

  44. final val rawPredictionCol: Param[String]

    Param for raw prediction (a.k.a.

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

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

    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( ... )
  46. final def set(paramPair: ParamPair[_]): BinaryClassificationEvaluator.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

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

    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
  48. final def set[T](param: Param[T], value: T): BinaryClassificationEvaluator.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

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

    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
  50. final def setDefault[T](param: Param[T], value: T): BinaryClassificationEvaluator.this.type

    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[ml]
    Definition Classes
    Params
  51. def setLabelCol(value: String): BinaryClassificationEvaluator.this.type

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

    Annotations
    @Since( "1.2.0" )
  53. def setNumBins(value: Int): BinaryClassificationEvaluator.this.type

    Annotations
    @Since( "3.0.0" )
  54. def setRawPredictionCol(value: String): BinaryClassificationEvaluator.this.type

    Annotations
    @Since( "1.5.0" )
  55. def setWeightCol(value: String): BinaryClassificationEvaluator.this.type

    Annotations
    @Since( "3.0.0" )
  56. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  57. def toString(): String
    Definition Classes
    BinaryClassificationEvaluator → Identifiable → AnyRef → Any
    Annotations
    @Since( "3.0.0" )
  58. val uid: String

    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    BinaryClassificationEvaluator → Identifiable
    Annotations
    @Since( "1.4.0" )
  59. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  60. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  61. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  62. final val weightCol: Param[String]

    Param for weight column name.

    Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.

    Definition Classes
    HasWeightCol
  63. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DefaultParamsWritable → MLWritable

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] ) @Deprecated
    Deprecated

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from HasWeightCol

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

(expert-only) Parameters

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

(expert-only) Parameter setters

(expert-only) Parameter getters