Class

com.microsoft.ml.spark.lightgbm

RegressorTrainParams

Related Doc: package lightgbm

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case class RegressorTrainParams(parallelism: String, topK: Int, numIterations: Int, learningRate: Double, numLeaves: Int, objective: String, 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]) 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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  1. RegressorTrainParams
  2. Product
  3. Equals
  4. TrainParams
  5. Serializable
  6. Serializable
  7. AnyRef
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Visibility
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Instance Constructors

  1. new RegressorTrainParams(parallelism: String, topK: Int, numIterations: Int, learningRate: Double, numLeaves: Int, objective: String, 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])

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Value Members

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

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  2. final def ##(): Int

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  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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  6. val baggingFraction: Double

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    RegressorTrainParams → TrainParams

  7. val baggingFreq: Int

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    RegressorTrainParams → TrainParams

  8. val baggingSeed: Int

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    Definition Classes
    RegressorTrainParams → TrainParams

  9. val binSampleCount: Int

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    Definition Classes
    RegressorTrainParams → TrainParams

  10. val boostFromAverage: Boolean

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  11. val boostingType: String

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    Definition Classes
    RegressorTrainParams → TrainParams

  12. val categoricalFeatures: Array[Int]

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    RegressorTrainParams → TrainParams

  13. def clone(): AnyRef

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    Attributes
    protected[java.lang]
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    @throws( ... )

  14. val delegate: Option[LightGBMDelegate]

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    Definition Classes
    RegressorTrainParams → TrainParams

  15. val earlyStoppingRound: Int

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    RegressorTrainParams → TrainParams

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

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    AnyRef

  17. val featureFraction: Double

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    RegressorTrainParams → TrainParams

  18. val featureNames: Array[String]

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    Definition Classes
    RegressorTrainParams → TrainParams

  19. def finalize(): Unit

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

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

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  21. val improvementTolerance: Double

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    RegressorTrainParams → TrainParams

  22. final def isInstanceOf[T0]: Boolean

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  23. val isProvideTrainingMetric: Boolean

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    RegressorTrainParams → TrainParams

  24. val lambdaL1: Double

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    RegressorTrainParams → TrainParams

  25. val lambdaL2: Double

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    RegressorTrainParams → TrainParams

  26. val learningRate: Double

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    RegressorTrainParams → TrainParams

  27. val maxBin: Int

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    RegressorTrainParams → TrainParams

  28. val maxBinByFeature: Array[Int]

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    RegressorTrainParams → TrainParams

  29. val maxDeltaStep: Double

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    RegressorTrainParams → TrainParams

  30. val maxDepth: Int

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    RegressorTrainParams → TrainParams

  31. val metric: String

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    RegressorTrainParams → TrainParams

  32. val minDataInLeaf: Int

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    RegressorTrainParams → TrainParams

  33. val minGainToSplit: Double

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    RegressorTrainParams → TrainParams

  34. val minSumHessianInLeaf: Double

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    RegressorTrainParams → TrainParams

  35. val modelString: Option[String]

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    RegressorTrainParams → TrainParams

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

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  37. val negBaggingFraction: Double

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    RegressorTrainParams → TrainParams

  38. final def notify(): Unit

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  39. final def notifyAll(): Unit

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  40. val numIterations: Int

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    RegressorTrainParams → TrainParams

  41. val numLeaves: Int

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    RegressorTrainParams → TrainParams

  42. val numMachines: Int

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    RegressorTrainParams → TrainParams

  43. val objective: String

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    RegressorTrainParams → TrainParams

  44. val parallelism: String

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    Definition Classes
    RegressorTrainParams → TrainParams

  45. val posBaggingFraction: Double

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    RegressorTrainParams → TrainParams

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

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  47. def toString(): String

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    RegressorTrainParams → TrainParams → AnyRef → Any

  48. val topK: Int

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    RegressorTrainParams → TrainParams

  49. val tweedieVariancePower: Double

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  50. val verbosity: Int

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    RegressorTrainParams → TrainParams

  51. final def wait(): Unit

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    @throws( ... )

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

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

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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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