c

com.microsoft.ml.spark.lightgbm.params

RegressorTrainParams

case class RegressorTrainParams(parallelism: String, topK: Int, numIterations: Int, learningRate: Double, numLeaves: Int, alpha: Double, tweedieVariancePower: Double, maxBin: Int, binSampleCount: Int, baggingFraction: Double, posBaggingFraction: Double, negBaggingFraction: Double, baggingFreq: Int, baggingSeed: Int, earlyStoppingRound: Int, improvementTolerance: Double, 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, minGainToSplit: Double, maxDeltaStep: Double, maxBinByFeature: Array[Int], minDataInLeaf: 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.

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
  1. Public
  2. All

Instance Constructors

  1. new RegressorTrainParams(parallelism: String, topK: Int, numIterations: Int, learningRate: Double, numLeaves: Int, alpha: Double, tweedieVariancePower: Double, maxBin: Int, binSampleCount: Int, baggingFraction: Double, posBaggingFraction: Double, negBaggingFraction: Double, baggingFreq: Int, baggingSeed: Int, earlyStoppingRound: Int, improvementTolerance: Double, 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, minGainToSplit: Double, maxDeltaStep: Double, maxBinByFeature: Array[Int], minDataInLeaf: 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: Double
    Definition Classes
    RegressorTrainParams → TrainParams
  7. val baggingFreq: Int
    Definition Classes
    RegressorTrainParams → TrainParams
  8. val baggingSeed: Int
    Definition Classes
    RegressorTrainParams → TrainParams
  9. val binSampleCount: Int
    Definition Classes
    RegressorTrainParams → TrainParams
  10. val boostFromAverage: Boolean
  11. val boostingType: String
    Definition Classes
    RegressorTrainParams → TrainParams
  12. val categoricalFeatures: Array[Int]
    Definition Classes
    RegressorTrainParams → TrainParams
  13. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  14. val dartModeParams: DartModeParams
    Definition Classes
    RegressorTrainParams → TrainParams
  15. val delegate: Option[LightGBMDelegate]
    Definition Classes
    RegressorTrainParams → TrainParams
  16. val earlyStoppingRound: Int
    Definition Classes
    RegressorTrainParams → TrainParams
  17. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  18. val executionParams: ExecutionParams
    Definition Classes
    RegressorTrainParams → TrainParams
  19. val featureFraction: Double
    Definition Classes
    RegressorTrainParams → TrainParams
  20. val featureNames: Array[String]
    Definition Classes
    RegressorTrainParams → TrainParams
  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
    RegressorTrainParams → TrainParams
  24. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  25. val isProvideTrainingMetric: Boolean
    Definition Classes
    RegressorTrainParams → TrainParams
  26. val lambdaL1: Double
    Definition Classes
    RegressorTrainParams → TrainParams
  27. val lambdaL2: Double
    Definition Classes
    RegressorTrainParams → TrainParams
  28. val learningRate: Double
    Definition Classes
    RegressorTrainParams → TrainParams
  29. val maxBin: Int
    Definition Classes
    RegressorTrainParams → TrainParams
  30. val maxBinByFeature: Array[Int]
    Definition Classes
    RegressorTrainParams → TrainParams
  31. val maxDeltaStep: Double
    Definition Classes
    RegressorTrainParams → TrainParams
  32. val maxDepth: Int
    Definition Classes
    RegressorTrainParams → TrainParams
  33. val metric: String
    Definition Classes
    RegressorTrainParams → TrainParams
  34. val minDataInLeaf: Int
    Definition Classes
    RegressorTrainParams → TrainParams
  35. val minGainToSplit: Double
    Definition Classes
    RegressorTrainParams → TrainParams
  36. val minSumHessianInLeaf: Double
    Definition Classes
    RegressorTrainParams → TrainParams
  37. val modelString: Option[String]
    Definition Classes
    RegressorTrainParams → TrainParams
  38. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  39. val negBaggingFraction: Double
    Definition Classes
    RegressorTrainParams → TrainParams
  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
    RegressorTrainParams → TrainParams
  43. val numLeaves: Int
    Definition Classes
    RegressorTrainParams → TrainParams
  44. val numMachines: Int
    Definition Classes
    RegressorTrainParams → TrainParams
  45. val objectiveParams: ObjectiveParams
    Definition Classes
    RegressorTrainParams → TrainParams
  46. val parallelism: String
    Definition Classes
    RegressorTrainParams → TrainParams
  47. val posBaggingFraction: Double
    Definition Classes
    RegressorTrainParams → TrainParams
  48. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  49. def toString(): String
    Definition Classes
    RegressorTrainParams → TrainParams → AnyRef → Any
  50. val topK: Int
    Definition Classes
    RegressorTrainParams → TrainParams
  51. val tweedieVariancePower: Double
  52. val verbosity: Int
    Definition Classes
    RegressorTrainParams → TrainParams
  53. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  54. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  55. 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

Ungrouped