trait DoubleMLParams extends Params with HasTreatmentCol with HasOutcomeCol with HasFeaturesCol with HasMaxIter with HasWeightCol with HasParallelismInjected

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Inherited
  1. DoubleMLParams
  2. HasParallelismInjected
  3. HasParallelism
  4. HasWeightCol
  5. HasMaxIter
  6. HasFeaturesCol
  7. HasOutcomeCol
  8. HasTreatmentCol
  9. Params
  10. Serializable
  11. Serializable
  12. Identifiable
  13. AnyRef
  14. Any
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Abstract Value Members

  1. abstract def copy(extra: ParamMap): Params
    Definition Classes
    Params
  2. abstract val uid: String
    Definition Classes
    Identifiable

Concrete Value Members

  1. final def clear(param: Param[_]): DoubleMLParams.this.type
    Definition Classes
    Params
  2. val confidenceLevel: DoubleParam
  3. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  4. def explainParams(): String
    Definition Classes
    Params
  5. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  6. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  7. val featuresCol: Param[String]

    The name of the features column

    The name of the features column

    Definition Classes
    HasFeaturesCol
  8. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  9. def getConfidenceLevel: Double
  10. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  11. def getExecutionContextProxy: ExecutionContext
    Definition Classes
    HasParallelismInjected
  12. def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  13. final def getMaxIter: Int
    Definition Classes
    HasMaxIter
  14. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  15. def getOutcomeCol: String
    Definition Classes
    HasOutcomeCol
  16. def getOutcomeModel: Estimator[_ <: Model[_]]
  17. def getParallelism: Int
    Definition Classes
    HasParallelism
  18. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  19. def getSampleSplitRatio: Array[Double]
  20. def getTreatmentCol: String
    Definition Classes
    HasTreatmentCol
  21. def getTreatmentModel: Estimator[_ <: Model[_]]
  22. def getWeightCol: String

    Definition Classes
    HasWeightCol
  23. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  24. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  25. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  26. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  27. final val maxIter: IntParam
    Definition Classes
    HasMaxIter
  28. val outcomeCol: Param[String]
    Definition Classes
    HasOutcomeCol
  29. val outcomeModel: EstimatorParam
  30. val parallelism: IntParam
    Definition Classes
    HasParallelism
  31. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  32. val sampleSplitRatio: DoubleArrayParam
  33. final def set[T](param: Param[T], value: T): DoubleMLParams.this.type
    Definition Classes
    Params
  34. def setConfidenceLevel(value: Double): DoubleMLParams.this.type

    Set the higher bound percentile of ATE distribution.

    Set the higher bound percentile of ATE distribution. Default is 0.975. lower bound value will be automatically calculated as 100*(1-confidenceLevel) That means by default we compute 95% confidence interval, it is [2.5%, 97.5%] percentile of ATE distribution

  35. def setFeaturesCol(value: String): DoubleMLParams.this.type

    Definition Classes
    HasFeaturesCol
  36. def setMaxIter(value: Int): DoubleMLParams.this.type

    Set the maximum number of confidence interval bootstrapping iterations.

    Set the maximum number of confidence interval bootstrapping iterations. Default is 1, which means it does not calculate confidence interval. To get Ci values please set a meaningful value

  37. def setOutcomeCol(value: String): DoubleMLParams.this.type

    Set name of the column which will be used as outcome

    Set name of the column which will be used as outcome

    Definition Classes
    HasOutcomeCol
  38. def setOutcomeModel(value: Estimator[_ <: Model[_]]): DoubleMLParams.this.type

    Set outcome model, it could be any model derived from 'org.apache.spark.ml.regression.Regressor' or 'org.apache.spark.ml.classification.ProbabilisticClassifier'

  39. def setParallelism(value: Int): DoubleMLParams.this.type
  40. def setSampleSplitRatio(value: Array[Double]): DoubleMLParams.this.type

    Set the sample split ratio, default is Array(0.5, 0.5)

  41. def setTreatmentCol(value: String): DoubleMLParams.this.type

    Set name of the column which will be used as treatment

    Set name of the column which will be used as treatment

    Definition Classes
    HasTreatmentCol
  42. def setTreatmentModel(value: Estimator[_ <: Model[_]]): DoubleMLParams.this.type

    Set treatment model, it could be any model derived from 'org.apache.spark.ml.regression.Regressor' or 'org.apache.spark.ml.classification.ProbabilisticClassifier'

  43. def setWeightCol(value: String): DoubleMLParams.this.type

    Definition Classes
    HasWeightCol
  44. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  45. val treatmentCol: Param[String]
    Definition Classes
    HasTreatmentCol
  46. val treatmentModel: EstimatorParam
  47. val weightCol: Param[String]

    The name of the weight column

    The name of the weight column

    Definition Classes
    HasWeightCol