OpMultiClassificationEvaluatorBase

abstract def evaluateAll(dataset: Dataset[_]): T

Evaluate function that returns a class or value with the calculated metric value(s).

abstract def getDefaultMetric: (T) ⇒ Double

Conversion from full metrics returned to a double value

abstract def name: EvalMetric

Name of evaluator

final def !=(arg0: Any): Boolean

final def ##(): Int

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

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

final def clear(param: Param[_]): OpMultiClassificationEvaluatorBase.this.type

def clone(): AnyRef

final def copy(extra: ParamMap): OpMultiClassificationEvaluatorBase.this.type

def copyValues[T <: Params](to: T, extra: ParamMap): T

final def defaultCopy[T <: Params](extra: ParamMap): T

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

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

Evaluate function that returns a single metric

def evaluate(dataset: Dataset[_], paramMap: ParamMap): Double

def explainParam(param: Param[_]): String

def explainParams(): String

final def extractParamMap(): ParamMap

final def extractParamMap(extra: ParamMap): ParamMap

def finalize(): Unit

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

final def getClass(): Class[_]

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

def getLabelCol: String

final def getOrDefault[T](param: Param[T]): T

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

final def getPredictionCol: String

final def getPredictionValueCol: String

final def getProbabilityCol: String

final def getRawPredictionCol: String

final def hasDefault[T](param: Param[T]): Boolean

def hasParam(paramName: String): Boolean

def hashCode(): Int

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

final def isInstanceOf[T0]: Boolean

def isLargerBetter: Boolean

Use the definition of the metric to determine if larger is better

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

final val labelCol: Param[String]

def makeDataToUse(data: Dataset[_], labelColName: String): Dataset[_]

Prepare data with different input types so that it can be consumed by the evaluator

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

lazy val params: Array[Param[_]]

final val predictionCol: Param[String]

final val predictionValueCol: Param[String]

final val probabilityCol: Param[String]

final val rawPredictionCol: Param[String]

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

final def set(param: String, value: Any): OpMultiClassificationEvaluatorBase.this.type

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

final def setDefault(paramPairs: ParamPair[_]*): OpMultiClassificationEvaluatorBase.this.type

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

def setLabelCol(value: FeatureLike[RealNN]): OpMultiClassificationEvaluatorBase.this.type

def setLabelCol(value: String): OpMultiClassificationEvaluatorBase.this.type

def setPredictionCol(value: FeatureLike[Prediction]): OpMultiClassificationEvaluatorBase.this.type

def setPredictionCol(value: String): OpMultiClassificationEvaluatorBase.this.type

def setPredictionValueCol(value: FeatureLike[RealNN]): OpMultiClassificationEvaluatorBase.this.type

def setPredictionValueCol(value: String): OpMultiClassificationEvaluatorBase.this.type

def setProbabilityCol(value: FeatureLike[OPVector]): OpMultiClassificationEvaluatorBase.this.type

def setProbabilityCol(value: String): OpMultiClassificationEvaluatorBase.this.type

def setRawPredictionCol(value: FeatureLike[OPVector]): OpMultiClassificationEvaluatorBase.this.type

def setRawPredictionCol(value: String): OpMultiClassificationEvaluatorBase.this.type

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

def toString(): String

val uid: String

final def wait(): Unit

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

final def wait(arg0: Long): Unit