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mimir.ml.spark

Regression

Related Doc: package spark

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object Regression extends SparkML

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SparkML, AnyRef, Any
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Type Members

  1. type DataFrameTransformer = (DataFrame) ⇒ DataFrame

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    Definition Classes
    SparkML
  2. type ValuePreparer = (PrimitiveValue, Type) ⇒ Any

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    Definition Classes
    SparkML

Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. def DecisionTreeRegressorModel(valuePreparer: ValuePreparer = prepareValueTrain, sparkTyper: (Type) ⇒ DataType = getSparkType): SparkModelGenerator

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  5. def GeneralizedLinearRegressorModel(valuePreparer: ValuePreparer = prepareValueTrain, sparkTyper: (Type) ⇒ DataType = getSparkType): SparkModelGenerator

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  6. def GradientBoostedTreeRegressorModel(valuePreparer: ValuePreparer = prepareValueTrain, sparkTyper: (Type) ⇒ DataType = getSparkType): SparkModelGenerator

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  7. def IsotonicRegressorModel(valuePreparer: ValuePreparer = prepareValueTrain, sparkTyper: (Type) ⇒ DataType = getSparkType): SparkModelGenerator

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  8. def LinearRegressorModel(valuePreparer: ValuePreparer = prepareValueTrain, sparkTyper: (Type) ⇒ DataType = getSparkType): SparkModelGenerator

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  9. def RandomForestRegressorModel(valuePreparer: ValuePreparer = prepareValueTrain, sparkTyper: (Type) ⇒ DataType = getSparkType): SparkModelGenerator

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  10. def applyModel(model: PipelineModel, cols: Seq[(String, Type)], testData: List[Seq[PrimitiveValue]], valuePreparer: ValuePreparer = prepareValueApply, sparkTyper: (Type) ⇒ DataType = getSparkType, dfTransformer: Option[DataFrameTransformer] = None): DataFrame

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    Definition Classes
    SparkML
  11. def applyModelDB(model: PipelineModel, query: Operator, db: Database, valuePreparer: ValuePreparer = prepareValueApply, sparkTyper: (Type) ⇒ DataType = getSparkType, dfTransformer: Option[DataFrameTransformer] = None): DataFrame

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    Definition Classes
    SparkML
  12. final def asInstanceOf[T0]: T0

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    Any
  13. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )
  14. final def eq(arg0: AnyRef): Boolean

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  15. def equals(arg0: Any): Boolean

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  16. def extractPredictions(model: PipelineModel, predictions: DataFrame, maxPredictions: Int = 5): Seq[(String, (String, Double))]

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    Definition Classes
    RegressionSparkML
  17. def extractPredictionsForRow(model: PipelineModel, predictions: DataFrame, rowid: String, maxPredictions: Int = 5): Seq[(String, Double)]

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    Definition Classes
    RegressionSparkML
  18. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  19. final def getClass(): Class[_]

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  20. def getSparkSession(): SparkContext

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    Definition Classes
    SparkML
  21. def getSparkSqlContext(): SQLContext

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    Definition Classes
    SparkML
  22. def getSparkType(t: Type): DataType

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    Definition Classes
    RegressionSparkML
  23. def hashCode(): Int

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  24. final def isInstanceOf[T0]: Boolean

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  25. final def ne(arg0: AnyRef): Boolean

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  26. final def notify(): Unit

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  27. final def notifyAll(): Unit

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  28. def nullValueReplacement(df: DataFrame): DataFrame

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    Attributes
    protected
    Definition Classes
    SparkML
  29. def prepareData(query: Operator, db: Database, valuePreparer: ValuePreparer = prepareValueTrain, sparkTyper: (Type) ⇒ DataType = getSparkType): DataFrame

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    Definition Classes
    SparkML
  30. def prepareValueApply(value: PrimitiveValue, t: Type): Any

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    Definition Classes
    RegressionSparkML
  31. def prepareValueTrain(value: PrimitiveValue, t: Type): Any

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    Definition Classes
    RegressionSparkML
  32. def regress(model: PipelineModel, cols: Seq[(String, Type)], testData: List[Seq[PrimitiveValue]], valuePreparer: ValuePreparer = prepareValueApply, sparkTyper: (Type) ⇒ DataType = getSparkType): DataFrame

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  33. def regressDB(model: PipelineModel, query: Operator, db: Database, valuePreparer: ValuePreparer = prepareValueApply, sparkTyper: (Type) ⇒ DataType = getSparkType): DataFrame

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  34. final def synchronized[T0](arg0: ⇒ T0): T0

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  35. def toString(): String

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  36. final def wait(): Unit

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    @throws( ... )
  37. final def wait(arg0: Long, arg1: Int): Unit

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    @throws( ... )
  38. final def wait(arg0: Long): Unit

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Inherited from SparkML

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