class ResidualTransformer extends Transformer with HasOutputCol with DefaultParamsWritable with Wrappable with SynapseMLLogging
Compute the differences between observed and predicted values of data. for classification, we compute residual as "observed - probability($(classIndex))" for regression, we compute residual as "observed - prediction"
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lazy val
classNameHelper: String
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def
clear(param: Param[_]): ResidualTransformer.this.type
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clone(): AnyRef
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companionModelClassName: String
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copy(extra: ParamMap): ResidualTransformer
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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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getDefault[T](param: Param[T]): Option[T]
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getOutputCol: String
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getParamInfo(p: Param[_]): ParamInfo[_]
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getPayload(methodName: String, numCols: Option[Int], executionSeconds: Option[Double], exception: Option[Exception]): Map[String, String]
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logBase(methodName: String, numCols: Option[Int], executionSeconds: Option[Double], featureName: Option[String]): Unit
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logClass(featureName: String): Unit
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logFit[T](f: ⇒ T, columns: Int): T
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val
outputCol: Param[String]
The name of the output column
The name of the output column
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params: Array[Param[_]]
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pyAdditionalMethods: String
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pyClassDoc: String
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pyClassName: String
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pyInitFunc(): String
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pyParamsArgs: String
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pyParamsDefaults: String
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pyParamsGetters: String
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pythonClass(): String
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rClass(): String
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rExtraInitLines: String
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rFuncName: String
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rInternalWrapper: Boolean
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rParamArg[T](p: Param[T]): String
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rParamsArgs: String
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rSetterLines: String
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def
save(path: String): Unit
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final
def
set(paramPair: ParamPair[_]): ResidualTransformer.this.type
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final
def
set(param: String, value: Any): ResidualTransformer.this.type
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final
def
set[T](param: Param[T], value: T): ResidualTransformer.this.type
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final
def
setDefault(paramPairs: ParamPair[_]*): ResidualTransformer.this.type
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final
def
setDefault[T](param: Param[T], value: T): ResidualTransformer.this.type
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- protected[org.apache.spark.ml]
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- Params
- def setObservedCol(value: String): ResidualTransformer.this.type
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def
setOutputCol(value: String): ResidualTransformer.this.type
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- def setPredictedCol(value: String): ResidualTransformer.this.type
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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val
thisStage: Params
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def
toString(): String
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def
transform(dataset: Dataset[_]): DataFrame
- Definition Classes
- ResidualTransformer → Transformer
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def
transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" )
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def
transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
- Definition Classes
- Transformer
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- @Since( "2.0.0" ) @varargs()
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def
transformSchema(schema: StructType): StructType
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- ResidualTransformer → PipelineStage
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def
transformSchema(schema: StructType, logging: Boolean): StructType
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val
uid: String
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- ResidualTransformer → SynapseMLLogging → Identifiable
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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def
write: MLWriter
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