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 val beta: DoubleParam

   The beta value, which controls precision vs recall weighting, used in "weightedFMeasure", "fMeasureByLabel".

   The beta value, which controls precision vs recall weighting, used in "weightedFMeasure", "fMeasureByLabel". Must be greater than 0. The default value is 1.

   Annotations

   @Since( "3.0.0" )

7.  final def clear(param: Param[_]): MulticlassClassificationEvaluator.this.type

   Clears the user-supplied value for the input param.

   Clears the user-supplied value for the input param.

   Definition Classes

   Params

8.  def clone(): AnyRef

   Attributes

   protected[lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( ... ) @native()

9.  def copy(extra: ParamMap): MulticlassClassificationEvaluator

   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

   MulticlassClassificationEvaluator → Evaluator → Params

   Annotations

   @Since( "1.5.0" )

10.  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

11.  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

12.  final val eps: DoubleParam

    param for eps.

    param for eps. log-loss is undefined for p=0 or p=1, so probabilities are clipped to max(eps, min(1 - eps, p)). Must be in range (0, 0.5). The default value is 1e-15.

    Annotations

    @Since( "3.0.0" )

13.  final def eq(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

14.  def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

15.  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

    MulticlassClassificationEvaluator → Evaluator

    Annotations

    @Since( "2.0.0" )

16.  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" )

17.  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

18.  def explainParams(): String

    Explains all params of this instance.

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

    Definition Classes

    Params

19.  final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes

    Params

20.  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

21.  def finalize(): Unit

    Attributes

    protected[lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

22.  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

23.  def getBeta: Double
    Annotations

    @Since( "3.0.0" )

24.  final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

25.  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

26.  def getEps: Double
    Annotations

    @Since( "3.0.0" )

27.  final def getLabelCol: String
    Definition Classes

    HasLabelCol

28.  def getMetricLabel: Double
    Annotations

    @Since( "3.0.0" )

29.  def getMetricName: String
    Annotations

    @Since( "1.5.0" )

30.  def getMetrics(dataset: Dataset[_]): MulticlassMetrics

    Get a MulticlassMetrics, which can be used to get multiclass classification metrics such as accuracy, weightedPrecision, etc.

    Get a MulticlassMetrics, which can be used to get multiclass classification metrics such as accuracy, weightedPrecision, etc.

    dataset

    a dataset that contains labels/observations and predictions.

    returns

    MulticlassMetrics

    Annotations

    @Since( "3.1.0" )

31.  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

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

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes

    Params

33.  final def getPredictionCol: String
    Definition Classes

    HasPredictionCol

34.  final def getProbabilityCol: String
    Definition Classes

    HasProbabilityCol

35.  final def getWeightCol: String
    Definition Classes

    HasWeightCol

36.  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

37.  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

38.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

39.  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

40.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

41.  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

    MulticlassClassificationEvaluator → Evaluator

    Annotations

    @Since( "1.5.0" )

42.  final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes

    Params

43.  final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes

    HasLabelCol

44.  final val metricLabel: DoubleParam

    The class whose metric will be computed in "truePositiveRateByLabel", "falsePositiveRateByLabel", "precisionByLabel", "recallByLabel", "fMeasureByLabel".

    The class whose metric will be computed in "truePositiveRateByLabel", "falsePositiveRateByLabel", "precisionByLabel", "recallByLabel", "fMeasureByLabel". Must be greater than or equal to 0. The default value is 0.

    Annotations

    @Since( "3.0.0" )

45.  val metricName: Param[String]

    param for metric name in evaluation (supports "f1" (default), "accuracy", "weightedPrecision", "weightedRecall", "weightedTruePositiveRate", "weightedFalsePositiveRate", "weightedFMeasure", "truePositiveRateByLabel", "falsePositiveRateByLabel", "precisionByLabel", "recallByLabel", "fMeasureByLabel", "logLoss", "hammingLoss")

    param for metric name in evaluation (supports "f1" (default), "accuracy", "weightedPrecision", "weightedRecall", "weightedTruePositiveRate", "weightedFalsePositiveRate", "weightedFMeasure", "truePositiveRateByLabel", "falsePositiveRateByLabel", "precisionByLabel", "recallByLabel", "fMeasureByLabel", "logLoss", "hammingLoss")

    Annotations

    @Since( "1.5.0" )

46.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

47.  final def notify(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

48.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

49.  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.

50.  final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes

    HasPredictionCol

51.  final val probabilityCol: Param[String]

    Param for Column name for predicted class conditional probabilities.

    Param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.

    Definition Classes

    HasProbabilityCol

52.  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( ... )

53.  final def set(paramPair: ParamPair[_]): MulticlassClassificationEvaluator.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes

    protected

    Definition Classes

    Params

54.  final def set(param: String, value: Any): MulticlassClassificationEvaluator.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

55.  final def set[T](param: Param[T], value: T): MulticlassClassificationEvaluator.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes

    Params

56.  def setBeta(value: Double): MulticlassClassificationEvaluator.this.type
    Annotations

    @Since( "3.0.0" )

57.  final def setDefault(paramPairs: ParamPair[_]*): MulticlassClassificationEvaluator.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

58.  final def setDefault[T](param: Param[T], value: T): MulticlassClassificationEvaluator.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

    Definition Classes

    Params

59.  def setEps(value: Double): MulticlassClassificationEvaluator.this.type
    Annotations

    @Since( "3.0.0" )

60.  def setLabelCol(value: String): MulticlassClassificationEvaluator.this.type
    Annotations

    @Since( "1.5.0" )

61.  def setMetricLabel(value: Double): MulticlassClassificationEvaluator.this.type
    Annotations

    @Since( "3.0.0" )

62.  def setMetricName(value: String): MulticlassClassificationEvaluator.this.type
    Annotations

    @Since( "1.5.0" )

63.  def setPredictionCol(value: String): MulticlassClassificationEvaluator.this.type
    Annotations

    @Since( "1.5.0" )

64.  def setProbabilityCol(value: String): MulticlassClassificationEvaluator.this.type
    Annotations

    @Since( "3.0.0" )

65.  def setWeightCol(value: String): MulticlassClassificationEvaluator.this.type
    Annotations

    @Since( "3.0.0" )

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

    Definition Classes

    AnyRef

67.  def toString(): String

    Definition Classes

    MulticlassClassificationEvaluator → Identifiable → AnyRef → Any

    Annotations

    @Since( "3.0.0" )

68.  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

    MulticlassClassificationEvaluator → Identifiable

    Annotations

    @Since( "1.5.0" )

69.  final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

71.  final def wait(arg0: Long): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... ) @native()

72.  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

73.  def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes

    DefaultParamsWritable → MLWritable