Class

com.microsoft.ml.spark.lightgbm

RankerTrainParams

Related Doc: package lightgbm

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case class RankerTrainParams(parallelism: String, topK: Int, numIterations: Int, learningRate: Double, numLeaves: Int, objective: String, 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], boostingType: String, lambdaL1: Double, lambdaL2: Double, maxPosition: Int, labelGain: Array[Double], isProvideTrainingMetric: Boolean, metric: String, evalAt: Array[Int], 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 ranker.

Linear Supertypes
Product, Equals, TrainParams, Serializable, Serializable, AnyRef, Any
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Inherited
  1. RankerTrainParams
  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 RankerTrainParams(parallelism: String, topK: Int, numIterations: Int, learningRate: Double, numLeaves: Int, objective: String, 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], boostingType: String, lambdaL1: Double, lambdaL2: Double, maxPosition: Int, labelGain: Array[Double], isProvideTrainingMetric: Boolean, metric: String, evalAt: Array[Int], 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. final def asInstanceOf[T0]: T0

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  5. val baggingFraction: Double

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    RankerTrainParams → TrainParams

  6. val baggingFreq: Int

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    Definition Classes
    RankerTrainParams → TrainParams

  7. val baggingSeed: Int

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    Definition Classes
    RankerTrainParams → TrainParams

  8. val binSampleCount: Int

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    Definition Classes
    RankerTrainParams → TrainParams

  9. val boostingType: String

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    Definition Classes
    RankerTrainParams → TrainParams

  10. val categoricalFeatures: Array[Int]

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    Definition Classes
    RankerTrainParams → TrainParams

  11. def clone(): AnyRef

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

  12. val delegate: Option[LightGBMDelegate]

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    Definition Classes
    RankerTrainParams → TrainParams

  13. val earlyStoppingRound: Int

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    RankerTrainParams → TrainParams

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

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    AnyRef

  15. val evalAt: Array[Int]

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  16. val featureFraction: Double

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    RankerTrainParams → TrainParams

  17. val featureNames: Array[String]

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    Definition Classes
    RankerTrainParams → TrainParams

  18. def finalize(): Unit

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

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

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  20. val improvementTolerance: Double

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    RankerTrainParams → TrainParams

  21. final def isInstanceOf[T0]: Boolean

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  22. val isProvideTrainingMetric: Boolean

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    RankerTrainParams → TrainParams

  23. val labelGain: Array[Double]

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  24. val lambdaL1: Double

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    RankerTrainParams → TrainParams

  25. val lambdaL2: Double

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    RankerTrainParams → TrainParams

  26. val learningRate: Double

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    RankerTrainParams → TrainParams

  27. val maxBin: Int

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    RankerTrainParams → TrainParams

  28. val maxBinByFeature: Array[Int]

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    RankerTrainParams → TrainParams

  29. val maxDeltaStep: Double

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    RankerTrainParams → TrainParams

  30. val maxDepth: Int

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    RankerTrainParams → TrainParams

  31. val maxPosition: Int

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  32. val metric: String

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    RankerTrainParams → TrainParams

  33. val minDataInLeaf: Int

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    RankerTrainParams → TrainParams

  34. val minGainToSplit: Double

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    RankerTrainParams → TrainParams

  35. val minSumHessianInLeaf: Double

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    RankerTrainParams → TrainParams

  36. val modelString: Option[String]

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    RankerTrainParams → TrainParams

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

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  38. val negBaggingFraction: Double

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    RankerTrainParams → TrainParams

  39. final def notify(): Unit

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  40. final def notifyAll(): Unit

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  41. val numIterations: Int

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    RankerTrainParams → TrainParams

  42. val numLeaves: Int

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    RankerTrainParams → TrainParams

  43. val numMachines: Int

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    RankerTrainParams → TrainParams

  44. val objective: String

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    RankerTrainParams → TrainParams

  45. val parallelism: String

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    Definition Classes
    RankerTrainParams → TrainParams

  46. val posBaggingFraction: Double

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    RankerTrainParams → TrainParams

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

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  48. def toString(): String

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    RankerTrainParams → TrainParams → AnyRef → Any

  49. val topK: Int

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    RankerTrainParams → TrainParams

  50. val verbosity: Int

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    RankerTrainParams → 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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