TrainedRegressorModel

Value Members

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

   

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   AnyRef → Any

2. final def ##(): Int

   

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3. final def $[T](param: Param[T]): T

   

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

   

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5. final def asInstanceOf[T0]: T0

   

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   Any

6. final def clear(param: Param[_]): TrainedRegressorModel.this.type

   

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7. def clone(): AnyRef

   

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   protected[java.lang]

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   @throws( ... )

8. def copy(extra: ParamMap): TrainedRegressorModel

   

   Definition Classes

   TrainedRegressorModel → Model → Transformer → PipelineStage → Params

9. def copyValues[T <: Params](to: T, extra: ParamMap): T

   

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10. final def defaultCopy[T <: Params](extra: ParamMap): T

    

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11. final def eq(arg0: AnyRef): Boolean

    

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12. def equals(arg0: Any): Boolean

    

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13. def explainParam(param: Param[_]): String

    

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14. def explainParams(): String

    

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15. final def extractParamMap(): ParamMap

    

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16. final def extractParamMap(extra: ParamMap): ParamMap

    

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17. val featuresColumn: String

    

    The features column

18. def finalize(): Unit

    

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19. final def get[T](param: Param[T]): Option[T]

    

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20. final def getClass(): Class[_]

    

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21. final def getDefault[T](param: Param[T]): Option[T]

    

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22. final def getOrDefault[T](param: Param[T]): T

    

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23. def getParam(paramName: String): Param[Any]

    

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24. def getParamMap: ParamMap

    

25. final def hasDefault[T](param: Param[T]): Boolean

    

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26. def hasParam(paramName: String): Boolean

    

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27. def hasParent: Boolean

    

    Definition Classes

    Model

28. def hashCode(): Int

    

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29. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

    

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    Logging

30. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

    

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    Logging

31. final def isDefined(param: Param[_]): Boolean

    

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32. final def isInstanceOf[T0]: Boolean

    

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33. final def isSet(param: Param[_]): Boolean

    

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34. def isTraceEnabled(): Boolean

    

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    Logging

35. val labelColumn: String

    

    The label column

36. def log: Logger

    

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37. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    

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38. def logDebug(msg: ⇒ String): Unit

    

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39. def logError(msg: ⇒ String, throwable: Throwable): Unit

    

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40. def logError(msg: ⇒ String): Unit

    

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41. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    

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42. def logInfo(msg: ⇒ String): Unit

    

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43. def logName: String

    

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44. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    

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    Logging

45. def logTrace(msg: ⇒ String): Unit

    

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    Logging

46. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    

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47. def logWarning(msg: ⇒ String): Unit

    

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48. val model: PipelineModel

    

    The trained model

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

    

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50. final def notify(): Unit

    

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51. final def notifyAll(): Unit

    

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52. val objectsToSave: List[Any]

    

    Definition Classes

    TrainedRegressorModel → ConstructorWritable

53. lazy val params: Array[Param[_]]

    

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    Params

54. var parent: Estimator[TrainedRegressorModel]

    

    Definition Classes

    Model

55. def save(path: String): Unit

    

    Definition Classes

    MLWritable

    Annotations

    @Since( "1.6.0" ) @throws( ... )

56. final def set(paramPair: ParamPair[_]): TrainedRegressorModel.this.type

    

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57. final def set(param: String, value: Any): TrainedRegressorModel.this.type

    

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58. final def set[T](param: Param[T], value: T): TrainedRegressorModel.this.type

    

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59. final def setDefault(paramPairs: ParamPair[_]*): TrainedRegressorModel.this.type

    

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60. final def setDefault[T](param: Param[T], value: T): TrainedRegressorModel.this.type

    

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61. def setParent(parent: Estimator[TrainedRegressorModel]): TrainedRegressorModel

    

    Definition Classes

    Model

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

    

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    AnyRef

63. def toString(): String

    

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    Identifiable → AnyRef → Any

64. def transform(dataset: Dataset[_]): DataFrame

    

    Definition Classes

    TrainedRegressorModel → Transformer

65. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

    

    Definition Classes

    Transformer

    Annotations

    @Since( "2.0.0" )

66. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

    

    Definition Classes

    Transformer

    Annotations

    @Since( "2.0.0" ) @varargs()

67. def transformSchema(schema: StructType): StructType

    

    Definition Classes

    TrainedRegressorModel → PipelineStage

    Annotations

    @DeveloperApi()

68. def transformSchema(schema: StructType, logging: Boolean): StructType

    

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    PipelineStage

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    @DeveloperApi()

69. val ttag: scala.reflect.api.JavaUniverse.TypeTag[TrainedRegressorModel]

    

    Definition Classes

    TrainedRegressorModel → ConstructorWritable

70. val uid: String

    

    The id of the module

    The id of the module

    Definition Classes

    TrainedRegressorModel → ConstructorWritable → Identifiable

71. final def wait(): Unit

    

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72. final def wait(arg0: Long, arg1: Int): Unit

    

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73. final def wait(arg0: Long): Unit

    

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    @throws( ... )

74. def write: MLWriter

    

    Definition Classes

    ConstructorWritable → MLWritable