trait DoubleMLParams extends Params with HasTreatmentCol with HasOutcomeCol with HasFeaturesCol with HasMaxIter with HasWeightCol with HasParallelismInjected
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- DoubleMLParams
- HasParallelismInjected
- HasParallelism
- HasWeightCol
- HasMaxIter
- HasFeaturesCol
- HasOutcomeCol
- HasTreatmentCol
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def
awaitFutures[T](futures: Array[Future[T]]): Seq[T]
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def
clear(param: Param[_]): DoubleMLParams.this.type
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clone(): AnyRef
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- @throws( ... ) @native()
- val confidenceLevel: DoubleParam
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copyValues[T <: Params](to: T, extra: ParamMap): T
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defaultCopy[T <: Params](extra: ParamMap): T
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explainParam(param: Param[_]): String
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explainParams(): String
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def
extractParamMap(): ParamMap
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final
def
extractParamMap(extra: ParamMap): ParamMap
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val
featuresCol: Param[String]
The name of the features column
The name of the features column
- Definition Classes
- HasFeaturesCol
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finalize(): Unit
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def
getDefault[T](param: Param[T]): Option[T]
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def
getExecutionContextProxy: ExecutionContext
- Definition Classes
- HasParallelismInjected
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def
getFeaturesCol: String
- Definition Classes
- HasFeaturesCol
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final
def
getMaxIter: Int
- Definition Classes
- HasMaxIter
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final
def
getOrDefault[T](param: Param[T]): T
- Definition Classes
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def
getOutcomeCol: String
- Definition Classes
- HasOutcomeCol
- def getOutcomeModel: Estimator[_ <: Model[_]]
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def
getParallelism: Int
- Definition Classes
- HasParallelism
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def
getParam(paramName: String): Param[Any]
- Definition Classes
- Params
- def getSampleSplitRatio: Array[Double]
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def
getTreatmentCol: String
- Definition Classes
- HasTreatmentCol
- def getTreatmentModel: Estimator[_ <: Model[_]]
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def
getWeightCol: String
- Definition Classes
- HasWeightCol
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final
def
hasDefault[T](param: Param[T]): Boolean
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def
hasParam(paramName: String): Boolean
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hashCode(): Int
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def
isDefined(param: Param[_]): Boolean
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isInstanceOf[T0]: Boolean
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def
isSet(param: Param[_]): Boolean
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final
val
maxIter: IntParam
- Definition Classes
- HasMaxIter
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notifyAll(): Unit
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val
outcomeCol: Param[String]
- Definition Classes
- HasOutcomeCol
- val outcomeModel: EstimatorParam
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val
parallelism: IntParam
- Definition Classes
- HasParallelism
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lazy val
params: Array[Param[_]]
- Definition Classes
- Params
- val sampleSplitRatio: DoubleArrayParam
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final
def
set(paramPair: ParamPair[_]): DoubleMLParams.this.type
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final
def
set(param: String, value: Any): DoubleMLParams.this.type
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final
def
set[T](param: Param[T], value: T): DoubleMLParams.this.type
- Definition Classes
- Params
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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
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final
def
setDefault(paramPairs: ParamPair[_]*): DoubleMLParams.this.type
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final
def
setDefault[T](param: Param[T], value: T): DoubleMLParams.this.type
- Attributes
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- Definition Classes
- Params
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def
setFeaturesCol(value: String): DoubleMLParams.this.type
- Definition Classes
- HasFeaturesCol
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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
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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
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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'
- def setParallelism(value: Int): DoubleMLParams.this.type
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def
setSampleSplitRatio(value: Array[Double]): DoubleMLParams.this.type
Set the sample split ratio, default is Array(0.5, 0.5)
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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
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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'
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def
setWeightCol(value: String): DoubleMLParams.this.type
- Definition Classes
- HasWeightCol
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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val
treatmentCol: Param[String]
- Definition Classes
- HasTreatmentCol
- val treatmentModel: EstimatorParam
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final
def
wait(): Unit
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def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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val
weightCol: Param[String]
The name of the weight column
The name of the weight column
- Definition Classes
- HasWeightCol