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com.microsoft.azure.synapse.ml.causal

OrthoForestDMLParams

trait OrthoForestDMLParams extends DoubleMLParams with HasNumTrees with HasMaxDepth with HasMinSampleLeaf with HasOutputCol

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Inherited
  1. OrthoForestDMLParams
  2. HasOutputCol
  3. HasMinSampleLeaf
  4. HasMaxDepth
  5. HasNumTrees
  6. DoubleMLParams
  7. HasParallelismInjected
  8. HasParallelism
  9. HasWeightCol
  10. HasMaxIter
  11. HasFeaturesCol
  12. HasOutcomeCol
  13. HasTreatmentCol
  14. Params
  15. Serializable
  16. Serializable
  17. Identifiable
  18. AnyRef
  19. Any
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Visibility
  1. Public
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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[_]): OrthoForestDMLParams.this.type
    Definition Classes
    Params
  2. val confidenceLevel: DoubleParam
    Definition Classes
    DoubleMLParams
  3. val confounderVecCol: Param[String]
  4. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  5. def explainParams(): String
    Definition Classes
    Params
  6. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  7. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  8. val featuresCol: Param[String]

    The name of the features column

    The name of the features column

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

    Definition Classes
    HasFeaturesCol
  15. def getHeterogeneityVecCol: String
  16. def getMaxDepth: Int
    Definition Classes
    HasMaxDepth
  17. final def getMaxIter: Int
    Definition Classes
    HasMaxIter
  18. def getMinSamplesLeaf: Int
    Definition Classes
    HasMinSampleLeaf
  19. def getNumTrees: Int
    Definition Classes
    HasNumTrees
  20. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  21. def getOutcomeCol: String
    Definition Classes
    HasOutcomeCol
  22. def getOutcomeModel: Estimator[_ <: Model[_]]
    Definition Classes
    DoubleMLParams
  23. def getOutcomeResidualCol: String
  24. def getOutputCol: String

    Definition Classes
    HasOutputCol
  25. def getOutputHighCol: String
  26. def getOutputLowCol: String
  27. def getParallelism: Int
    Definition Classes
    HasParallelism
  28. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  29. def getSampleSplitRatio: Array[Double]
    Definition Classes
    DoubleMLParams
  30. def getTreatmentCol: String
    Definition Classes
    HasTreatmentCol
  31. def getTreatmentModel: Estimator[_ <: Model[_]]
    Definition Classes
    DoubleMLParams
  32. def getTreatmentResidualCol: String
  33. def getWeightCol: String

    Definition Classes
    HasWeightCol
  34. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  35. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  36. val heterogeneityVecCol: Param[String]
  37. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  38. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  39. val maxDepth: IntParam
    Definition Classes
    HasMaxDepth
  40. final val maxIter: IntParam
    Definition Classes
    HasMaxIter
  41. val minSamplesLeaf: IntParam
    Definition Classes
    HasMinSampleLeaf
  42. val numTrees: IntParam
    Definition Classes
    HasNumTrees
  43. val outcomeCol: Param[String]
    Definition Classes
    HasOutcomeCol
  44. val outcomeModel: EstimatorParam
    Definition Classes
    DoubleMLParams
  45. val outcomeResidualCol: Param[String]
  46. val outputCol: Param[String]

    The name of the output column

    The name of the output column

    Definition Classes
    HasOutputCol
  47. val outputHighCol: Param[String]
  48. val outputLowCol: Param[String]
  49. val parallelism: IntParam
    Definition Classes
    HasParallelism
  50. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  51. val sampleSplitRatio: DoubleArrayParam
    Definition Classes
    DoubleMLParams
  52. final def set[T](param: Param[T], value: T): OrthoForestDMLParams.this.type
    Definition Classes
    Params
  53. def setConfidenceLevel(value: Double): OrthoForestDMLParams.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

    Definition Classes
    DoubleMLParams
  54. def setConfounderVecCol(value: String): OrthoForestDMLParams.this.type

    Set confounder vector column

  55. def setFeaturesCol(value: String): OrthoForestDMLParams.this.type

    Definition Classes
    HasFeaturesCol
  56. def setHeterogeneityVecCol(value: String): OrthoForestDMLParams.this.type

    Set heterogeneity vector column

  57. def setMaxDepth(value: Int): OrthoForestDMLParams.this.type

    Set max depth of the trees to be used in the forest

    Set max depth of the trees to be used in the forest

    Definition Classes
    HasMaxDepth
  58. def setMaxIter(value: Int): OrthoForestDMLParams.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

    Definition Classes
    DoubleMLParams
  59. def setMinSamplesLeaf(value: Int): OrthoForestDMLParams.this.type

    Set number of samples in the leaf node of trees to be used in the forest

    Set number of samples in the leaf node of trees to be used in the forest

    Definition Classes
    HasMinSampleLeaf
  60. def setNumTrees(value: Int): OrthoForestDMLParams.this.type

    Set number of trees to be used in the forest

    Set number of trees to be used in the forest

    Definition Classes
    HasNumTrees
  61. def setOutcomeCol(value: String): OrthoForestDMLParams.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
  62. def setOutcomeModel(value: Estimator[_ <: Model[_]]): OrthoForestDMLParams.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'

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

    Definition Classes
    DoubleMLParams
  63. def setOutcomeResidualCol(value: String): OrthoForestDMLParams.this.type

    Set outcome residual column

  64. def setOutputCol(value: String): OrthoForestDMLParams.this.type

    Definition Classes
    HasOutputCol
  65. def setOutputHighCol(value: String): OrthoForestDMLParams.this.type

    Set output column for effect upper bound

  66. def setOutputLowCol(value: String): OrthoForestDMLParams.this.type

    Set output column for effect lower bound

  67. def setParallelism(value: Int): OrthoForestDMLParams.this.type
    Definition Classes
    DoubleMLParams
  68. def setSampleSplitRatio(value: Array[Double]): OrthoForestDMLParams.this.type

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

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

    Definition Classes
    DoubleMLParams
  69. def setTreatmentCol(value: String): OrthoForestDMLParams.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
  70. def setTreatmentModel(value: Estimator[_ <: Model[_]]): OrthoForestDMLParams.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'

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

    Definition Classes
    DoubleMLParams
  71. def setTreatmentResidualCol(value: String): OrthoForestDMLParams.this.type

    Set treatment residual column

  72. def setWeightCol(value: String): OrthoForestDMLParams.this.type

    Definition Classes
    HasWeightCol
  73. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  74. val treatmentCol: Param[String]
    Definition Classes
    HasTreatmentCol
  75. val treatmentModel: EstimatorParam
    Definition Classes
    DoubleMLParams
  76. val treatmentResidualCol: Param[String]
  77. val weightCol: Param[String]

    The name of the weight column

    The name of the weight column

    Definition Classes
    HasWeightCol