c

com.microsoft.azure.synapse.ml.lightgbm.params

RegressorTrainParams

case class RegressorTrainParams(parallelism: String, topK: Option[Int], numIterations: Int, learningRate: Double, numLeaves: Option[Int], alpha: Double, tweedieVariancePower: Double, maxBin: Option[Int], binSampleCount: Option[Int], baggingFraction: Option[Double], posBaggingFraction: Option[Double], negBaggingFraction: Option[Double], baggingFreq: Option[Int], baggingSeed: Option[Int], earlyStoppingRound: Int, improvementTolerance: Double, featureFraction: Option[Double], maxDepth: Option[Int], minSumHessianInLeaf: Option[Double], numMachines: Int, modelString: Option[String], verbosity: Int, categoricalFeatures: Array[Int], boostFromAverage: Boolean, boostingType: String, lambdaL1: Option[Double], lambdaL2: Option[Double], isProvideTrainingMetric: Option[Boolean], metric: Option[String], minGainToSplit: Option[Double], maxDeltaStep: Option[Double], maxBinByFeature: Array[Int], minDataInLeaf: Option[Int], featureNames: Array[String], delegate: Option[LightGBMDelegate], dartModeParams: DartModeParams, executionParams: ExecutionParams, objectiveParams: ObjectiveParams) extends TrainParams with Product with Serializable

Defines the Booster parameters passed to the LightGBM regressor.

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Inherited
  1. RegressorTrainParams
  2. Product
  3. Equals
  4. TrainParams
  5. Serializable
  6. Serializable
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  8. Any
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Instance Constructors

  1. new RegressorTrainParams(parallelism: String, topK: Option[Int], numIterations: Int, learningRate: Double, numLeaves: Option[Int], alpha: Double, tweedieVariancePower: Double, maxBin: Option[Int], binSampleCount: Option[Int], baggingFraction: Option[Double], posBaggingFraction: Option[Double], negBaggingFraction: Option[Double], baggingFreq: Option[Int], baggingSeed: Option[Int], earlyStoppingRound: Int, improvementTolerance: Double, featureFraction: Option[Double], maxDepth: Option[Int], minSumHessianInLeaf: Option[Double], numMachines: Int, modelString: Option[String], verbosity: Int, categoricalFeatures: Array[Int], boostFromAverage: Boolean, boostingType: String, lambdaL1: Option[Double], lambdaL2: Option[Double], isProvideTrainingMetric: Option[Boolean], metric: Option[String], minGainToSplit: Option[Double], maxDeltaStep: Option[Double], maxBinByFeature: Array[Int], minDataInLeaf: Option[Int], featureNames: Array[String], delegate: Option[LightGBMDelegate], dartModeParams: DartModeParams, executionParams: ExecutionParams, objectiveParams: ObjectiveParams)

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. val alpha: Double
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. val baggingFraction: Option[Double]
    Definition Classes
    RegressorTrainParamsTrainParams
  7. val baggingFreq: Option[Int]
    Definition Classes
    RegressorTrainParamsTrainParams
  8. val baggingSeed: Option[Int]
    Definition Classes
    RegressorTrainParamsTrainParams
  9. val binSampleCount: Option[Int]
    Definition Classes
    RegressorTrainParamsTrainParams
  10. val boostFromAverage: Boolean
  11. val boostingType: String
    Definition Classes
    RegressorTrainParamsTrainParams
  12. val categoricalFeatures: Array[Int]
    Definition Classes
    RegressorTrainParamsTrainParams
  13. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  14. val dartModeParams: DartModeParams
    Definition Classes
    RegressorTrainParamsTrainParams
  15. val delegate: Option[LightGBMDelegate]
    Definition Classes
    RegressorTrainParamsTrainParams
  16. val earlyStoppingRound: Int
    Definition Classes
    RegressorTrainParamsTrainParams
  17. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  18. val executionParams: ExecutionParams
    Definition Classes
    RegressorTrainParamsTrainParams
  19. val featureFraction: Option[Double]
    Definition Classes
    RegressorTrainParamsTrainParams
  20. val featureNames: Array[String]
    Definition Classes
    RegressorTrainParamsTrainParams
  21. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  22. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  23. val improvementTolerance: Double
    Definition Classes
    RegressorTrainParamsTrainParams
  24. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  25. val isProvideTrainingMetric: Option[Boolean]
    Definition Classes
    RegressorTrainParamsTrainParams
  26. val lambdaL1: Option[Double]
    Definition Classes
    RegressorTrainParamsTrainParams
  27. val lambdaL2: Option[Double]
    Definition Classes
    RegressorTrainParamsTrainParams
  28. val learningRate: Double
    Definition Classes
    RegressorTrainParamsTrainParams
  29. val maxBin: Option[Int]
    Definition Classes
    RegressorTrainParamsTrainParams
  30. val maxBinByFeature: Array[Int]
    Definition Classes
    RegressorTrainParamsTrainParams
  31. val maxDeltaStep: Option[Double]
    Definition Classes
    RegressorTrainParamsTrainParams
  32. val maxDepth: Option[Int]
    Definition Classes
    RegressorTrainParamsTrainParams
  33. val metric: Option[String]
    Definition Classes
    RegressorTrainParamsTrainParams
  34. val minDataInLeaf: Option[Int]
    Definition Classes
    RegressorTrainParamsTrainParams
  35. val minGainToSplit: Option[Double]
    Definition Classes
    RegressorTrainParamsTrainParams
  36. val minSumHessianInLeaf: Option[Double]
    Definition Classes
    RegressorTrainParamsTrainParams
  37. val modelString: Option[String]
    Definition Classes
    RegressorTrainParamsTrainParams
  38. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  39. val negBaggingFraction: Option[Double]
    Definition Classes
    RegressorTrainParamsTrainParams
  40. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  41. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  42. val numIterations: Int
    Definition Classes
    RegressorTrainParamsTrainParams
  43. val numLeaves: Option[Int]
    Definition Classes
    RegressorTrainParamsTrainParams
  44. val numMachines: Int
    Definition Classes
    RegressorTrainParamsTrainParams
  45. val objectiveParams: ObjectiveParams
    Definition Classes
    RegressorTrainParamsTrainParams
  46. val parallelism: String
    Definition Classes
    RegressorTrainParamsTrainParams
  47. def paramToString[T](paramName: String, paramValueOpt: Option[T]): String
    Definition Classes
    TrainParams
  48. def paramsToString(paramNamesToValues: Array[(String, Option[_])]): String
    Definition Classes
    TrainParams
  49. val posBaggingFraction: Option[Double]
    Definition Classes
    RegressorTrainParamsTrainParams
  50. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  51. def toString(): String
    Definition Classes
    RegressorTrainParamsTrainParams → AnyRef → Any
  52. val topK: Option[Int]
    Definition Classes
    RegressorTrainParamsTrainParams
  53. val tweedieVariancePower: Double
  54. val verbosity: Int
    Definition Classes
    RegressorTrainParamsTrainParams
  55. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  56. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  57. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()

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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