trait OrthoForestDMLParams extends DoubleMLParams with HasNumTrees with HasMaxDepth with HasMinSampleLeaf with HasOutputCol
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- OrthoForestDMLParams
- HasOutputCol
- HasMinSampleLeaf
- HasMaxDepth
- HasNumTrees
- DoubleMLParams
- HasParallelismInjected
- HasParallelism
- HasWeightCol
- HasMaxIter
- HasFeaturesCol
- HasOutcomeCol
- HasTreatmentCol
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def
clear(param: Param[_]): OrthoForestDMLParams.this.type
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clone(): AnyRef
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val
confidenceLevel: DoubleParam
- Definition Classes
- DoubleMLParams
- val confounderVecCol: Param[String]
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copyValues[T <: Params](to: T, 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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getClass(): Class[_]
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def
getConfidenceLevel: Double
- Definition Classes
- DoubleMLParams
- def getConfounderVecCol: String
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final
def
getDefault[T](param: Param[T]): Option[T]
- Definition Classes
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def
getExecutionContextProxy: ExecutionContext
- Definition Classes
- HasParallelismInjected
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def
getFeaturesCol: String
- Definition Classes
- HasFeaturesCol
- def getHeterogeneityVecCol: String
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def
getMaxDepth: Int
- Definition Classes
- HasMaxDepth
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final
def
getMaxIter: Int
- Definition Classes
- HasMaxIter
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def
getMinSamplesLeaf: Int
- Definition Classes
- HasMinSampleLeaf
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def
getNumTrees: Int
- Definition Classes
- HasNumTrees
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final
def
getOrDefault[T](param: Param[T]): T
- Definition Classes
- Params
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def
getOutcomeCol: String
- Definition Classes
- HasOutcomeCol
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def
getOutcomeModel: Estimator[_ <: Model[_]]
- Definition Classes
- DoubleMLParams
- def getOutcomeResidualCol: String
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def
getOutputCol: String
- Definition Classes
- HasOutputCol
- def getOutputHighCol: String
- def getOutputLowCol: String
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def
getParallelism: Int
- Definition Classes
- HasParallelism
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def
getParam(paramName: String): Param[Any]
- Definition Classes
- Params
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def
getSampleSplitRatio: Array[Double]
- Definition Classes
- DoubleMLParams
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def
getTreatmentCol: String
- Definition Classes
- HasTreatmentCol
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def
getTreatmentModel: Estimator[_ <: Model[_]]
- Definition Classes
- DoubleMLParams
- def getTreatmentResidualCol: String
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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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def
hashCode(): Int
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- val heterogeneityVecCol: Param[String]
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def
isDefined(param: Param[_]): Boolean
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isInstanceOf[T0]: Boolean
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final
def
isSet(param: Param[_]): Boolean
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- Params
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val
maxDepth: IntParam
- Definition Classes
- HasMaxDepth
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final
val
maxIter: IntParam
- Definition Classes
- HasMaxIter
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val
minSamplesLeaf: IntParam
- Definition Classes
- HasMinSampleLeaf
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final
def
ne(arg0: AnyRef): Boolean
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def
notify(): Unit
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def
notifyAll(): Unit
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val
numTrees: IntParam
- Definition Classes
- HasNumTrees
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val
outcomeCol: Param[String]
- Definition Classes
- HasOutcomeCol
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val
outcomeModel: EstimatorParam
- Definition Classes
- DoubleMLParams
- val outcomeResidualCol: Param[String]
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val
outputCol: Param[String]
The name of the output column
The name of the output column
- Definition Classes
- HasOutputCol
- val outputHighCol: Param[String]
- val outputLowCol: Param[String]
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val
parallelism: IntParam
- Definition Classes
- HasParallelism
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lazy val
params: Array[Param[_]]
- Definition Classes
- Params
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val
sampleSplitRatio: DoubleArrayParam
- Definition Classes
- DoubleMLParams
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final
def
set(paramPair: ParamPair[_]): OrthoForestDMLParams.this.type
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- protected
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final
def
set(param: String, value: Any): OrthoForestDMLParams.this.type
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final
def
set[T](param: Param[T], value: T): OrthoForestDMLParams.this.type
- Definition Classes
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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
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def
setConfounderVecCol(value: String): OrthoForestDMLParams.this.type
Set confounder vector column
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final
def
setDefault(paramPairs: ParamPair[_]*): OrthoForestDMLParams.this.type
- Attributes
- protected
- Definition Classes
- Params
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final
def
setDefault[T](param: Param[T], value: T): OrthoForestDMLParams.this.type
- Attributes
- protected[org.apache.spark.ml]
- Definition Classes
- Params
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def
setFeaturesCol(value: String): OrthoForestDMLParams.this.type
- Definition Classes
- HasFeaturesCol
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def
setHeterogeneityVecCol(value: String): OrthoForestDMLParams.this.type
Set heterogeneity vector column
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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
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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
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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
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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
-
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
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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
-
def
setOutcomeResidualCol(value: String): OrthoForestDMLParams.this.type
Set outcome residual column
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def
setOutputCol(value: String): OrthoForestDMLParams.this.type
- Definition Classes
- HasOutputCol
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def
setOutputHighCol(value: String): OrthoForestDMLParams.this.type
Set output column for effect upper bound
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def
setOutputLowCol(value: String): OrthoForestDMLParams.this.type
Set output column for effect lower bound
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def
setParallelism(value: Int): OrthoForestDMLParams.this.type
- Definition Classes
- DoubleMLParams
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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
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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
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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
-
def
setTreatmentResidualCol(value: String): OrthoForestDMLParams.this.type
Set treatment residual column
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def
setWeightCol(value: String): OrthoForestDMLParams.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
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val
treatmentModel: EstimatorParam
- Definition Classes
- DoubleMLParams
- val treatmentResidualCol: Param[String]
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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