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.

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: 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. val alpha: Double
  2. val baggingFraction: Option[Double]
    Definition Classes
    RegressorTrainParams → TrainParams
  3. val baggingFreq: Option[Int]
    Definition Classes
    RegressorTrainParams → TrainParams
  4. val baggingSeed: Option[Int]
    Definition Classes
    RegressorTrainParams → TrainParams
  5. val binSampleCount: Option[Int]
    Definition Classes
    RegressorTrainParams → TrainParams
  6. val boostFromAverage: Boolean
  7. val boostingType: String
    Definition Classes
    RegressorTrainParams → TrainParams
  8. val categoricalFeatures: Array[Int]
    Definition Classes
    RegressorTrainParams → TrainParams
  9. val dartModeParams: DartModeParams
    Definition Classes
    RegressorTrainParams → TrainParams
  10. val delegate: Option[LightGBMDelegate]
    Definition Classes
    RegressorTrainParams → TrainParams
  11. val earlyStoppingRound: Int
    Definition Classes
    RegressorTrainParams → TrainParams
  12. val executionParams: ExecutionParams
    Definition Classes
    RegressorTrainParams → TrainParams
  13. val featureFraction: Option[Double]
    Definition Classes
    RegressorTrainParams → TrainParams
  14. val featureNames: Array[String]
    Definition Classes
    RegressorTrainParams → TrainParams
  15. val improvementTolerance: Double
    Definition Classes
    RegressorTrainParams → TrainParams
  16. val isProvideTrainingMetric: Option[Boolean]
    Definition Classes
    RegressorTrainParams → TrainParams
  17. val lambdaL1: Option[Double]
    Definition Classes
    RegressorTrainParams → TrainParams
  18. val lambdaL2: Option[Double]
    Definition Classes
    RegressorTrainParams → TrainParams
  19. val learningRate: Double
    Definition Classes
    RegressorTrainParams → TrainParams
  20. val maxBin: Option[Int]
    Definition Classes
    RegressorTrainParams → TrainParams
  21. val maxBinByFeature: Array[Int]
    Definition Classes
    RegressorTrainParams → TrainParams
  22. val maxDeltaStep: Option[Double]
    Definition Classes
    RegressorTrainParams → TrainParams
  23. val maxDepth: Option[Int]
    Definition Classes
    RegressorTrainParams → TrainParams
  24. val metric: Option[String]
    Definition Classes
    RegressorTrainParams → TrainParams
  25. val minDataInLeaf: Option[Int]
    Definition Classes
    RegressorTrainParams → TrainParams
  26. val minGainToSplit: Option[Double]
    Definition Classes
    RegressorTrainParams → TrainParams
  27. val minSumHessianInLeaf: Option[Double]
    Definition Classes
    RegressorTrainParams → TrainParams
  28. val modelString: Option[String]
    Definition Classes
    RegressorTrainParams → TrainParams
  29. val negBaggingFraction: Option[Double]
    Definition Classes
    RegressorTrainParams → TrainParams
  30. val numIterations: Int
    Definition Classes
    RegressorTrainParams → TrainParams
  31. val numLeaves: Option[Int]
    Definition Classes
    RegressorTrainParams → TrainParams
  32. val numMachines: Int
    Definition Classes
    RegressorTrainParams → TrainParams
  33. val objectiveParams: ObjectiveParams
    Definition Classes
    RegressorTrainParams → TrainParams
  34. val parallelism: String
    Definition Classes
    RegressorTrainParams → TrainParams
  35. def paramToString[T](paramName: String, paramValueOpt: Option[T]): String
    Definition Classes
    TrainParams
  36. def paramsToString(paramNamesToValues: Array[(String, Option[_])]): String
    Definition Classes
    TrainParams
  37. val posBaggingFraction: Option[Double]
    Definition Classes
    RegressorTrainParams → TrainParams
  38. def toString(): String
    Definition Classes
    RegressorTrainParams → TrainParams → AnyRef → Any
  39. val topK: Option[Int]
    Definition Classes
    RegressorTrainParams → TrainParams
  40. val tweedieVariancePower: Double
  41. val verbosity: Int
    Definition Classes
    RegressorTrainParams → TrainParams