Class

com.salesforce.op.stages.impl.regression

OpLinearRegression

Related Doc: package regression

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class OpLinearRegression extends OpPredictorWrapper[LinearRegression, LinearRegressionModel] with OpLinearRegressionParams

Wrapper around spark ml linear regression org.apache.spark.ml.regression.LinearRegression

Linear Supertypes
OpLinearRegressionParams, LinearRegressionParams, HasLoss, HasAggregationDepth, HasSolver, HasWeightCol, HasStandardization, HasFitIntercept, HasTol, HasMaxIter, HasElasticNetParam, HasRegParam, PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, OpPredictorWrapper[LinearRegression, LinearRegressionModel], SparkWrapperParams[LinearRegression], OpPipelineStage2[RealNN, OPVector, Prediction], HasOut[Prediction], HasIn2, HasIn1, OpPipelineStage[Prediction], OpPipelineStageBase, MLWritable, OpPipelineStageParams, InputParams, Estimator[OpPredictorWrapperModel[LinearRegressionModel]], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. OpLinearRegression
  2. OpLinearRegressionParams
  3. LinearRegressionParams
  4. HasLoss
  5. HasAggregationDepth
  6. HasSolver
  7. HasWeightCol
  8. HasStandardization
  9. HasFitIntercept
  10. HasTol
  11. HasMaxIter
  12. HasElasticNetParam
  13. HasRegParam
  14. PredictorParams
  15. HasPredictionCol
  16. HasFeaturesCol
  17. HasLabelCol
  18. OpPredictorWrapper
  19. SparkWrapperParams
  20. OpPipelineStage2
  21. HasOut
  22. HasIn2
  23. HasIn1
  24. OpPipelineStage
  25. OpPipelineStageBase
  26. MLWritable
  27. OpPipelineStageParams
  28. InputParams
  29. Estimator
  30. PipelineStage
  31. Logging
  32. Params
  33. Serializable
  34. Serializable
  35. Identifiable
  36. AnyRef
  37. Any
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Visibility
  1. Public
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Instance Constructors

  1. new OpLinearRegression(uid: String = UID[OpLinearRegression])

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Type Members

  1. final type InputFeatures = (FeatureLike[RealNN], FeatureLike[OPVector])

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    Input Features type

    Input Features type

    Definition Classes
    OpPipelineStage2OpPipelineStageInputParams
  2. final type OutputFeatures = FeatureLike[Prediction]

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    Definition Classes
    OpPipelineStageOpPipelineStageBase

Value Members

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

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

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    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  5. final val aggregationDepth: IntParam

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    Definition Classes
    HasAggregationDepth
  6. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  7. final def checkInputLength(features: Array[_]): Boolean

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    Checks the input length

    Checks the input length

    features

    input features

    returns

    true is input size as expected, false otherwise

    Definition Classes
    OpPipelineStage2InputParams
  8. def checkSerializable: Try[Unit]

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    Check if the stage is serializable

    Check if the stage is serializable

    returns

    Failure if not serializable

    Definition Classes
    OpPipelineStageBase
  9. final def clear(param: Param[_]): OpLinearRegression.this.type

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    Definition Classes
    Params
  10. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  11. final def copy(extra: ParamMap): OpLinearRegression.this.type

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    This method is used to make a copy of the instance with new parameters in several methods in spark internals Default will find the constructor and make a copy for any class (AS LONG AS ALL CONSTRUCTOR PARAMS ARE VALS, this is why type tags are written as implicit vals in base classes).

    This method is used to make a copy of the instance with new parameters in several methods in spark internals Default will find the constructor and make a copy for any class (AS LONG AS ALL CONSTRUCTOR PARAMS ARE VALS, this is why type tags are written as implicit vals in base classes).

    Note: that the convention in spark is to have the uid be a constructor argument, so that copies will share a uid with the original (developers should follow this convention).

    extra

    new parameters want to add to instance

    returns

    a new instance with the same uid

    Definition Classes
    OpPipelineStageBase → Params
  12. def copyValues[T <: Params](to: T, extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params
  13. final def defaultCopy[T <: Params](extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params
  14. final val elasticNetParam: DoubleParam

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    Definition Classes
    HasElasticNetParam
  15. final val epsilon: DoubleParam

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    Definition Classes
    LinearRegressionParams
    Annotations
    @Since( "2.3.0" )
  16. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  17. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  18. def explainParam(param: Param[_]): String

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    Definition Classes
    Params
  19. def explainParams(): String

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    Definition Classes
    Params
  20. final def extractParamMap(): ParamMap

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    Definition Classes
    Params
  21. final def extractParamMap(extra: ParamMap): ParamMap

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    Definition Classes
    Params
  22. final val featuresCol: Param[String]

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    Definition Classes
    HasFeaturesCol
  23. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  24. def fit(dataset: Dataset[_]): OpPredictorWrapperModel[LinearRegressionModel]

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    Function that fits the binary model

    Function that fits the binary model

    Definition Classes
    OpPredictorWrapper → Estimator
  25. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[OpPredictorWrapperModel[LinearRegressionModel]]

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  26. def fit(dataset: Dataset[_], paramMap: ParamMap): OpPredictorWrapperModel[LinearRegressionModel]

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  27. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): OpPredictorWrapperModel[LinearRegressionModel]

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  28. final val fitIntercept: BooleanParam

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    Definition Classes
    HasFitIntercept
  29. final def get[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  30. final def getAggregationDepth: Int

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    Definition Classes
    HasAggregationDepth
  31. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  32. final def getDefault[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  33. final def getElasticNetParam: Double

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    Definition Classes
    HasElasticNetParam
  34. def getEpsilon: Double

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    Definition Classes
    LinearRegressionParams
    Annotations
    @Since( "2.3.0" )
  35. final def getFeaturesCol: String

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    Definition Classes
    HasFeaturesCol
  36. final def getFitIntercept: Boolean

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    Definition Classes
    HasFitIntercept
  37. def getInputColParamNames(): Array[String]

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    Gets names of parameters that control input columns for Spark stage

    Gets names of parameters that control input columns for Spark stage

    Definition Classes
    SparkWrapperParams
  38. final def getInputFeature[T <: FeatureType](i: Int): Option[FeatureLike[T]]

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    Gets an input feature Note: this method IS NOT safe to use outside the driver, please use getTransientFeature method instead

    Gets an input feature Note: this method IS NOT safe to use outside the driver, please use getTransientFeature method instead

    returns

    array of features

    Definition Classes
    InputParams
    Exceptions thrown

    NoSuchElementException if the features are not set

    RuntimeException in case one of the features is null

  39. final def getInputFeatures(): Array[OPFeature]

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    Gets the input features Note: this method IS NOT safe to use outside the driver, please use getTransientFeatures method instead

    Gets the input features Note: this method IS NOT safe to use outside the driver, please use getTransientFeatures method instead

    returns

    array of features

    Definition Classes
    InputParams
    Exceptions thrown

    NoSuchElementException if the features are not set

    RuntimeException in case one of the features is null

  40. final def getInputSchema(): StructType

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    Definition Classes
    OpPipelineStageParams
  41. final def getLabelCol: String

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    Definition Classes
    HasLabelCol
  42. final def getLoss: String

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    Definition Classes
    HasLoss
  43. final def getMaxIter: Int

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    Definition Classes
    HasMaxIter
  44. final def getMetadata(): Metadata

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    Definition Classes
    OpPipelineStageParams
  45. final def getOrDefault[T](param: Param[T]): T

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    Definition Classes
    Params
  46. def getOutput(): FeatureLike[Prediction]

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    Output features that will be created by this stage

    Output features that will be created by this stage

    returns

    feature of type OutputFeatures

    Definition Classes
    HasOut → OpPipelineStageBase
  47. def getOutputColParamNames(): Array[String]

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    Gets names of parameters that control output columns for Spark stage

    Gets names of parameters that control output columns for Spark stage

    Definition Classes
    SparkWrapperParams
  48. final def getOutputFeatureName: String

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    Name of output feature (i.e.

    Name of output feature (i.e. column created by this stage)

    Definition Classes
    OpPipelineStage
  49. def getParam(paramName: String): Param[Any]

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    Definition Classes
    Params
  50. final def getPredictionCol: String

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    Definition Classes
    HasPredictionCol
  51. final def getRegParam: Double

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    Definition Classes
    HasRegParam
  52. final def getSolver: String

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    Definition Classes
    HasSolver
  53. def getSparkMlStage(): Option[LinearRegression]

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    Method to access the spark stage being wrapped

    Method to access the spark stage being wrapped

    returns

    Option of spark ml stage

    Definition Classes
    SparkWrapperParams
  54. def getStageSavePath(): Option[String]

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    Gets a save path for wrapped spark stage

    Gets a save path for wrapped spark stage

    Definition Classes
    SparkWrapperParams
  55. final def getStandardization: Boolean

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    Definition Classes
    HasStandardization
  56. final def getTol: Double

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    Definition Classes
    HasTol
  57. final def getTransientFeature(i: Int): Option[TransientFeature]

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    Gets an input feature at index i

    Gets an input feature at index i

    i

    input index

    returns

    maybe an input feature

    Definition Classes
    InputParams
  58. final def getTransientFeatures(): Array[TransientFeature]

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    Gets the input Features

    Gets the input Features

    returns

    input features

    Definition Classes
    InputParams
  59. final def getWeightCol: String

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    Definition Classes
    HasWeightCol
  60. final def hasDefault[T](param: Param[T]): Boolean

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    Definition Classes
    Params
  61. def hasParam(paramName: String): Boolean

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    Definition Classes
    Params
  62. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  63. final def in1: TransientFeature

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    Attributes
    protected
    Definition Classes
    HasIn1
  64. final def in2: TransientFeature

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    Attributes
    protected
    Definition Classes
    HasIn2
  65. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  66. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  67. final def inputAsArray(in: InputFeatures): Array[OPFeature]

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    Function to convert InputFeatures to an Array of FeatureLike

    Function to convert InputFeatures to an Array of FeatureLike

    returns

    an Array of FeatureLike

    Definition Classes
    OpPipelineStage2InputParams
  68. val inputParam1Name: String

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    Definition Classes
    OpPredictorWrapper
  69. val inputParam2Name: String

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    Definition Classes
    OpPredictorWrapper
  70. final def isDefined(param: Param[_]): Boolean

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    Definition Classes
    Params
  71. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  72. final def isSet(param: Param[_]): Boolean

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    Definition Classes
    Params
  73. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  74. final val labelCol: Param[String]

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    Definition Classes
    HasLabelCol
  75. def log: Logger

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    Attributes
    protected
    Definition Classes
    Logging
  76. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  77. def logDebug(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  78. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  79. def logError(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  80. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  81. def logInfo(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  82. def logName: String

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    Attributes
    protected
    Definition Classes
    Logging
  83. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  84. def logTrace(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  85. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  86. def logWarning(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  87. final val loss: Param[String]

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    Definition Classes
    LinearRegressionParams → HasLoss
    Annotations
    @Since( "2.3.0" )
  88. final val maxIter: IntParam

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    Definition Classes
    HasMaxIter
  89. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  90. final def notify(): Unit

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    Definition Classes
    AnyRef
  91. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  92. def onGetMetadata(): Unit

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    Function to be called on getMetadata

    Function to be called on getMetadata

    Attributes
    protected
    Definition Classes
    OpPipelineStageParams
  93. def onSetInput(): Unit

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    Function to be called on setInput

    Function to be called on setInput

    Attributes
    protected
    Definition Classes
    OpLinearRegressionInputParams
  94. val operationName: String

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    Short unique name of the operation this stage performs

    Short unique name of the operation this stage performs

    returns

    operation name

    Definition Classes
    OpPredictorWrapperOpPipelineStageBase
  95. final def outputAsArray(out: OutputFeatures): Array[OPFeature]

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    Function to convert OutputFeatures to an Array of FeatureLike

    Function to convert OutputFeatures to an Array of FeatureLike

    returns

    an Array of FeatureLike

    Definition Classes
    OpPipelineStageOpPipelineStageBase
  96. def outputFeatureUid: String

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    Attributes
    protected[com.salesforce.op]
    Definition Classes
    OpPipelineStage2OpPipelineStage
  97. def outputIsResponse: Boolean

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    Should output feature be a response? Yes, if any of the input features are.

    Should output feature be a response? Yes, if any of the input features are.

    returns

    true if the the output feature should be a response

    Definition Classes
    OpPipelineStage
  98. val outputParamName: String

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    Definition Classes
    OpPredictorWrapper
  99. lazy val params: Array[Param[_]]

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    Definition Classes
    Params
  100. final val predictionCol: Param[String]

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    Definition Classes
    HasPredictionCol
  101. val predictor: LinearRegression

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    the predictor to wrap

    the predictor to wrap

    Definition Classes
    OpPredictorWrapper
  102. final val regParam: DoubleParam

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    Definition Classes
    HasRegParam
  103. def save(path: String): Unit

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    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  104. final def set(paramPair: ParamPair[_]): OpLinearRegression.this.type

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    Attributes
    protected
    Definition Classes
    Params
  105. final def set(param: String, value: Any): OpLinearRegression.this.type

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    Attributes
    protected
    Definition Classes
    Params
  106. final def set[T](param: Param[T], value: T): OpLinearRegression.this.type

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    Definition Classes
    Params
  107. def setAggregationDepth(value: Int): OpLinearRegression.this.type

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    Suggested depth for treeAggregate (greater than or equal to 2).

    Suggested depth for treeAggregate (greater than or equal to 2). If the dimensions of features or the number of partitions are large, this param could be adjusted to a larger size. Default is 2.

  108. final def setDefault(paramPairs: ParamPair[_]*): OpLinearRegression.this.type

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    Attributes
    protected
    Definition Classes
    Params
  109. final def setDefault[T](param: Param[T], value: T): OpLinearRegression.this.type

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    Attributes
    protected
    Definition Classes
    Params
  110. def setElasticNetParam(value: Double): OpLinearRegression.this.type

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    Set the ElasticNet mixing parameter.

    Set the ElasticNet mixing parameter. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty. For alpha in (0,1), the penalty is a combination of L1 and L2. Default is 0.0 which is an L2 penalty.

  111. def setEpsilon(value: Double): OpLinearRegression.this.type

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    The shape parameter to control the amount of robustness.

    The shape parameter to control the amount of robustness. Must be > 1.0. At larger values of epsilon, the huber criterion becomes more similar to least squares regression; for small values of epsilon, the criterion is more similar to L1 regression. Default is 1.35 to get as much robustness as possible while retaining 95% statistical efficiency for normally distributed data. It matches sklearn HuberRegressor and is "M" from A robust hybrid of lasso and ridge regression. Only valid when "loss" is "huber".

  112. def setFitIntercept(value: Boolean): OpLinearRegression.this.type

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    Set if we should fit the intercept.

    Set if we should fit the intercept. Default is true.

  113. final def setInput(features: InputFeatures): OpLinearRegression.this.type

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    Input features that will be used by the stage

    Input features that will be used by the stage

    returns

    feature of type InputFeatures

    Definition Classes
    OpPipelineStageBase
  114. final def setInputFeatures[S <: OPFeature](features: Array[S]): OpLinearRegression.this.type

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    Sets input features

    Sets input features

    S

    feature like type

    features

    array of input features

    returns

    this stage

    Attributes
    protected
    Definition Classes
    InputParams
  115. def setLossType(value: String): OpLinearRegression.this.type

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    Set the type of loss function to be optimized.

    Set the type of loss function to be optimized. Supported options: "squaredError" (https://en.wikipedia.org/wiki/Mean_squared_error) and "huber" (https://en.wikipedia.org/wiki/Huber_loss). Default is squaredError.

  116. def setMaxIter(value: Int): OpLinearRegression.this.type

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    Set the maximum number of iterations.

    Set the maximum number of iterations. Default is 100.

  117. final def setMetadata(m: Metadata): OpLinearRegression.this.type

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    Definition Classes
    OpPipelineStageParams
  118. def setOutputFeatureName(name: String): OpLinearRegression.this.type

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    Definition Classes
    OpPipelineStage
  119. def setRegParam(value: Double): OpLinearRegression.this.type

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    Set the regularization parameter.

    Set the regularization parameter. Default is 0.0.

  120. def setSolver(value: String): OpLinearRegression.this.type

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    Set the solver algorithm used for optimization.

    Set the solver algorithm used for optimization. In case of linear regression, this can be "l-bfgs", "normal" and "auto".

    • "l-bfgs" denotes Limited-memory BFGS which is a limited-memory quasi-Newton optimization method.
    • "normal" denotes using Normal Equation as an analytical solution to the linear regression problem. This solver is limited to LinearRegression.MAX_FEATURES_FOR_NORMAL_SOLVER.
    • "auto" (default) means that the solver algorithm is selected automatically. The Normal Equations solver will be used when possible, but this will automatically fall back to iterative optimization methods when needed.
  121. def setSparkMlStage(stage: Option[LinearRegression]): OpLinearRegression.this.type

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    Attributes
    protected
    Definition Classes
    SparkWrapperParams
  122. def setStageSavePath(path: String): OpLinearRegression.this.type

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    Sets a save path for wrapped spark stage

    Sets a save path for wrapped spark stage

    Definition Classes
    SparkWrapperParams
  123. def setStandardization(value: Boolean): OpLinearRegression.this.type

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    Whether to standardize the training features before fitting the model.

    Whether to standardize the training features before fitting the model. The coefficients of models will be always returned on the original scale, so it will be transparent for users. Default is true.

    Note

    With/without standardization, the models should be always converged to the same solution when no regularization is applied. In R's GLMNET package, the default behavior is true as well.

  124. def setTol(value: Double): OpLinearRegression.this.type

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    Set the convergence tolerance of iterations.

    Set the convergence tolerance of iterations. Smaller value will lead to higher accuracy with the cost of more iterations. Default is 1E-6.

  125. def setWeightCol(value: String): OpLinearRegression.this.type

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    Whether to over-/under-sample training instances according to the given weights in weightCol.

    Whether to over-/under-sample training instances according to the given weights in weightCol. If not set or empty, all instances are treated equally (weight 1.0). Default is not set, so all instances have weight one.

  126. final val solver: Param[String]

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    Definition Classes
    LinearRegressionParams → HasSolver
    Annotations
    @Since( "1.6.0" )
  127. final val sparkInputColParamNames: StringArrayParam

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    Definition Classes
    SparkWrapperParams
  128. final val sparkMlStage: SparkStageParam[LinearRegression]

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    Definition Classes
    SparkWrapperParams
  129. final val sparkOutputColParamNames: StringArrayParam

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    Definition Classes
    SparkWrapperParams
  130. final def stageName: String

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    Stage unique name consisting of the stage operation name and uid

    Stage unique name consisting of the stage operation name and uid

    returns

    stage name

    Definition Classes
    OpPipelineStageBase
  131. final val standardization: BooleanParam

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    Definition Classes
    HasStandardization
  132. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  133. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any
  134. final val tol: DoubleParam

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    Definition Classes
    HasTol
  135. final def transformSchema(schema: StructType): StructType

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    This function translates the input and output features into spark schema checks and changes that will occur on the underlying data frame

    This function translates the input and output features into spark schema checks and changes that will occur on the underlying data frame

    schema

    schema of the input data frame

    returns

    a new schema with the output features added

    Definition Classes
    OpPipelineStageBase
  136. def transformSchema(schema: StructType, logging: Boolean): StructType

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    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  137. implicit val tti1: scala.reflect.api.JavaUniverse.TypeTag[RealNN]

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    Definition Classes
    OpPredictorWrapper
  138. implicit val tti2: scala.reflect.api.JavaUniverse.TypeTag[OPVector]

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    Definition Classes
    OpPredictorWrapper
  139. implicit val tto: scala.reflect.api.JavaUniverse.TypeTag[Prediction]

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    Type tag of the output

    Type tag of the output

    Definition Classes
    OpPredictorWrapper → HasOut
  140. implicit val ttov: scala.reflect.api.JavaUniverse.TypeTag[Map[String, Double]]

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    Type tag of the output value

    Type tag of the output value

    Definition Classes
    OpPredictorWrapper → HasOut
  141. val uid: String

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    stage uid

    stage uid

    Definition Classes
    OpPredictorWrapper → Identifiable
  142. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

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    Attributes
    protected
    Definition Classes
    LinearRegressionParams → PredictorParams
  143. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  144. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  145. final def wait(arg0: Long): Unit

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  146. final val weightCol: Param[String]

    Permalink
    Definition Classes
    HasWeightCol
  147. final def write: MLWriter

    Permalink
    Definition Classes
    OpPipelineStageBase → MLWritable

Inherited from OpLinearRegressionParams

Inherited from LinearRegressionParams

Inherited from HasLoss

Inherited from HasAggregationDepth

Inherited from HasSolver

Inherited from HasWeightCol

Inherited from HasStandardization

Inherited from HasFitIntercept

Inherited from HasTol

Inherited from HasMaxIter

Inherited from HasElasticNetParam

Inherited from HasRegParam

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from OpPredictorWrapper[LinearRegression, LinearRegressionModel]

Inherited from SparkWrapperParams[LinearRegression]

Inherited from HasOut[Prediction]

Inherited from HasIn2

Inherited from HasIn1

Inherited from OpPipelineStage[Prediction]

Inherited from OpPipelineStageBase

Inherited from MLWritable

Inherited from OpPipelineStageParams

Inherited from InputParams

Inherited from Estimator[OpPredictorWrapperModel[LinearRegressionModel]]

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

expertParam

expertSetParam

setParam

Ungrouped