Class

com.microsoft.ml.spark.lightgbm

RegressorTrainParams

Related Doc: package lightgbm

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case class RegressorTrainParams(parallelism: String, numIterations: Int, learningRate: Double, numLeaves: Int, objective: String, alpha: Double, tweedieVariancePower: Double, maxBin: Int, baggingFraction: Double, baggingFreq: Int, baggingSeed: Int, earlyStoppingRound: Int, featureFraction: Double, maxDepth: Int, minSumHessianInLeaf: Double, numMachines: Int, modelString: Option[String], verbosity: Int, categoricalFeatures: Array[Int], boostFromAverage: Boolean, boostingType: String, lambdaL1: Double, lambdaL2: Double, isProvideTrainingMetric: Boolean, metric: String) extends TrainParams with Product with Serializable

Defines the Booster parameters passed to the LightGBM regressor.

Linear Supertypes
Product, Equals, TrainParams, Serializable, Serializable, AnyRef, Any
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Inherited
  1. RegressorTrainParams
  2. Product
  3. Equals
  4. TrainParams
  5. Serializable
  6. Serializable
  7. AnyRef
  8. Any
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Visibility
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Instance Constructors

  1. new RegressorTrainParams(parallelism: String, numIterations: Int, learningRate: Double, numLeaves: Int, objective: String, alpha: Double, tweedieVariancePower: Double, maxBin: Int, baggingFraction: Double, baggingFreq: Int, baggingSeed: Int, earlyStoppingRound: Int, featureFraction: Double, maxDepth: Int, minSumHessianInLeaf: Double, numMachines: Int, modelString: Option[String], verbosity: Int, categoricalFeatures: Array[Int], boostFromAverage: Boolean, boostingType: String, lambdaL1: Double, lambdaL2: Double, isProvideTrainingMetric: Boolean, metric: String)

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Value Members

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

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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    AnyRef → Any

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

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  4. val alpha: Double

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  5. final def asInstanceOf[T0]: T0

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    Any

  6. val baggingFraction: Double

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    RegressorTrainParams → TrainParams

  7. val baggingFreq: Int

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    Definition Classes
    RegressorTrainParams → TrainParams

  8. val baggingSeed: Int

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    Definition Classes
    RegressorTrainParams → TrainParams

  9. val boostFromAverage: Boolean

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  10. val boostingType: String

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    Definition Classes
    RegressorTrainParams → TrainParams

  11. val categoricalFeatures: Array[Int]

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    Definition Classes
    RegressorTrainParams → TrainParams

  12. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  13. val earlyStoppingRound: Int

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    Definition Classes
    RegressorTrainParams → TrainParams

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

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    AnyRef

  15. val featureFraction: Double

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    Definition Classes
    RegressorTrainParams → TrainParams

  16. def finalize(): Unit

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    protected[java.lang]
    Definition Classes
    AnyRef
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    @throws( classOf[java.lang.Throwable] )

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

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    AnyRef → Any

  18. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  19. val isProvideTrainingMetric: Boolean

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    Definition Classes
    RegressorTrainParams → TrainParams

  20. val lambdaL1: Double

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    Definition Classes
    RegressorTrainParams → TrainParams

  21. val lambdaL2: Double

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    RegressorTrainParams → TrainParams

  22. val learningRate: Double

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    Definition Classes
    RegressorTrainParams → TrainParams

  23. val maxBin: Int

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    RegressorTrainParams → TrainParams

  24. val maxDepth: Int

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    Definition Classes
    RegressorTrainParams → TrainParams

  25. val metric: String

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    Definition Classes
    RegressorTrainParams → TrainParams

  26. val minSumHessianInLeaf: Double

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    Definition Classes
    RegressorTrainParams → TrainParams

  27. val modelString: Option[String]

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    Definition Classes
    RegressorTrainParams → TrainParams

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

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    AnyRef

  29. final def notify(): Unit

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    AnyRef

  30. final def notifyAll(): Unit

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    AnyRef

  31. val numIterations: Int

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    RegressorTrainParams → TrainParams

  32. val numLeaves: Int

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    Definition Classes
    RegressorTrainParams → TrainParams

  33. val numMachines: Int

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    Definition Classes
    RegressorTrainParams → TrainParams

  34. val objective: String

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    Definition Classes
    RegressorTrainParams → TrainParams

  35. val parallelism: String

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    Definition Classes
    RegressorTrainParams → TrainParams

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

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    Definition Classes
    AnyRef

  37. def toString(): String

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    RegressorTrainParams → TrainParams → AnyRef → Any

  38. val tweedieVariancePower: Double

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  39. val verbosity: Int

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    Definition Classes
    RegressorTrainParams → TrainParams

  40. final def wait(): Unit

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    Annotations
    @throws( ... )

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

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    @throws( ... )

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

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    Annotations
    @throws( ... )

Inherited from Product

Inherited from Equals

Inherited from TrainParams

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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