class LightGBMRegressionModel extends RegressionModel[Vector, LightGBMRegressionModel] with LightGBMModelParams with LightGBMModelMethods with LightGBMPredictionParams with ComplexParamsWritable with SynapseMLLogging

Model produced by LightGBMRegressor.

Linear Supertypes
SynapseMLLogging, ComplexParamsWritable, MLWritable, LightGBMPredictionParams, LightGBMModelMethods, LightGBMModelParams, Wrappable, DotnetWrappable, RWrappable, PythonWrappable, BaseWrappable, RegressionModel[Vector, LightGBMRegressionModel], PredictionModel[Vector, LightGBMRegressionModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Model[LightGBMRegressionModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. LightGBMRegressionModel
  2. SynapseMLLogging
  3. ComplexParamsWritable
  4. MLWritable
  5. LightGBMPredictionParams
  6. LightGBMModelMethods
  7. LightGBMModelParams
  8. Wrappable
  9. DotnetWrappable
  10. RWrappable
  11. PythonWrappable
  12. BaseWrappable
  13. RegressionModel
  14. PredictionModel
  15. PredictorParams
  16. HasPredictionCol
  17. HasFeaturesCol
  18. HasLabelCol
  19. Model
  20. Transformer
  21. PipelineStage
  22. Logging
  23. Params
  24. Serializable
  25. Serializable
  26. Identifiable
  27. AnyRef
  28. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new LightGBMRegressionModel()
  2. new LightGBMRegressionModel(uid: String)

Value Members

  1. final def clear(param: Param[_]): LightGBMRegressionModel.this.type
    Definition Classes
    Params
  2. def copy(extra: ParamMap): LightGBMRegressionModel
    Definition Classes
    LightGBMRegressionModel → Model → Transformer → PipelineStage → Params
  3. def dotnetAdditionalMethods: String
    Definition Classes
    DotnetWrappable
  4. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  5. def explainParams(): String
    Definition Classes
    Params
  6. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  7. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  8. final val featuresCol: Param[String]
    Definition Classes
    HasFeaturesCol
  9. val featuresShapCol: Param[String]
    Definition Classes
    LightGBMPredictionParams
  10. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  11. def getBoosterBestIteration(): Int

    Public method to get the best iteration from the booster.

    Public method to get the best iteration from the booster.

    returns

    The best iteration, if early stopping was triggered.

    Definition Classes
    LightGBMModelMethods
  12. def getBoosterNumClasses(): Int

    Public method to get the number of classes from the booster.

    Public method to get the number of classes from the booster.

    returns

    The number of classes.

    Definition Classes
    LightGBMModelMethods
  13. def getBoosterNumFeatures(): Int

    Public method to get the number of features from the booster.

    Public method to get the number of features from the booster.

    returns

    The number of features.

    Definition Classes
    LightGBMModelMethods
  14. def getBoosterNumTotalIterations(): Int

    Public method to get the total number of iterations trained.

    Public method to get the total number of iterations trained.

    returns

    The total number of iterations trained.

    Definition Classes
    LightGBMModelMethods
  15. def getBoosterNumTotalModel(): Int

    Public method to get the total number of models trained.

    Public method to get the total number of models trained. Note this may be larger than the number of iterations, since in multiclass a model is trained per class for each iteration.

    returns

    The total number of models.

    Definition Classes
    LightGBMModelMethods
  16. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  17. def getDenseFeatureShaps(features: Array[Double]): Array[Double]

    Public method for pyspark API to get the dense local SHAP feature importance values for an instance.

    Public method for pyspark API to get the dense local SHAP feature importance values for an instance.

    features

    The local instance or row to compute the SHAP values for.

    returns

    The local feature importance values.

    Definition Classes
    LightGBMModelMethods
  18. def getFeatureImportances(importanceType: String): Array[Double]

    Public method to get the global feature importance values.

    Public method to get the global feature importance values.

    importanceType

    split or gini

    returns

    The global feature importance values.

    Definition Classes
    LightGBMModelMethods
  19. def getFeatureShaps(features: Vector): Array[Double]

    Public method to get the vector local SHAP feature importance values for an instance.

    Public method to get the vector local SHAP feature importance values for an instance.

    features

    The local instance or row to compute the SHAP values for.

    returns

    The local feature importance values.

    Definition Classes
    LightGBMModelMethods
  20. final def getFeaturesCol: String
    Definition Classes
    HasFeaturesCol
  21. def getFeaturesShapCol: String
    Definition Classes
    LightGBMPredictionParams
  22. final def getLabelCol: String
    Definition Classes
    HasLabelCol
  23. def getLeafPredictionCol: String
    Definition Classes
    LightGBMPredictionParams
  24. def getLightGBMBooster: LightGBMBooster
    Definition Classes
    LightGBMModelParams
  25. def getModel: LightGBMBooster

    Alias for same method

    Alias for same method

    returns

    The LightGBM Booster.

    Definition Classes
    LightGBMModelParams
  26. def getNativeModel(): String

    Gets the native model serialized representation as a string.

    Gets the native model serialized representation as a string.

    Definition Classes
    LightGBMModelMethods
  27. def getNumIterations: Int
    Definition Classes
    LightGBMModelParams
  28. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  29. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  30. def getParamInfo(p: Param[_]): ParamInfo[_]
    Definition Classes
    BaseWrappable
  31. def getPredictDisableShapeCheck: Boolean
    Definition Classes
    LightGBMPredictionParams
  32. final def getPredictionCol: String
    Definition Classes
    HasPredictionCol
  33. def getSparseFeatureShaps(size: Int, indices: Array[Int], values: Array[Double]): Array[Double]

    Public method for pyspark API to get the sparse local SHAP feature importance values for an instance.

    Public method for pyspark API to get the sparse local SHAP feature importance values for an instance.

    returns

    The local feature importance values.

    Definition Classes
    LightGBMModelMethods
  34. def getStartIteration: Int
    Definition Classes
    LightGBMModelParams
  35. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  36. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  37. def hasParent: Boolean
    Definition Classes
    Model
  38. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  39. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  40. final val labelCol: Param[String]
    Definition Classes
    HasLabelCol
  41. val leafPredictionCol: Param[String]
    Definition Classes
    LightGBMPredictionParams
  42. val lightGBMBooster: LightGBMBoosterParam
    Definition Classes
    LightGBMModelParams
  43. def logClass(featureName: String): Unit
    Definition Classes
    SynapseMLLogging
  44. def logFit[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  45. def logTransform[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  46. def logVerb[T](verb: String, f: ⇒ T, columns: Option[Int] = None): T
    Definition Classes
    SynapseMLLogging
  47. def makeDotnetFile(conf: CodegenConfig): Unit
    Definition Classes
    DotnetWrappable
  48. def makePyFile(conf: CodegenConfig): Unit
    Definition Classes
    PythonWrappable
  49. def makeRFile(conf: CodegenConfig): Unit
    Definition Classes
    RWrappable
  50. def numFeatures: Int
    Definition Classes
    PredictionModel
    Annotations
    @Since( "1.6.0" )
  51. val numIterations: IntParam
    Definition Classes
    LightGBMModelParams
  52. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  53. var parent: Estimator[LightGBMRegressionModel]
    Definition Classes
    Model
  54. def predict(features: Vector): Double
    Definition Classes
    LightGBMRegressionModel → PredictionModel
  55. val predictDisableShapeCheck: BooleanParam
    Definition Classes
    LightGBMPredictionParams
  56. final val predictionCol: Param[String]
    Definition Classes
    HasPredictionCol
  57. def pyAdditionalMethods: String
    Definition Classes
    PythonWrappable
  58. def pyInitFunc(): String
    Definition Classes
    PythonWrappable
  59. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  60. def saveNativeModel(filename: String, overwrite: Boolean): Unit

    Saves the native model serialized representation to file.

    Saves the native model serialized representation to file.

    filename

    The name of the file to save the model to

    overwrite

    Whether to overwrite if the file already exists

    Definition Classes
    LightGBMModelMethods
  61. final def set[T](param: Param[T], value: T): LightGBMRegressionModel.this.type
    Definition Classes
    Params
  62. def setFeaturesCol(value: String): LightGBMRegressionModel
    Definition Classes
    PredictionModel
  63. def setFeaturesShapCol(value: String): LightGBMRegressionModel.this.type
    Definition Classes
    LightGBMPredictionParams
  64. def setLeafPredictionCol(value: String): LightGBMRegressionModel.this.type
    Definition Classes
    LightGBMPredictionParams
  65. def setLightGBMBooster(value: LightGBMBooster): LightGBMRegressionModel.this.type
    Definition Classes
    LightGBMModelParams
  66. def setNumIterations(value: Int): LightGBMRegressionModel.this.type
    Definition Classes
    LightGBMModelParams
  67. def setParent(parent: Estimator[LightGBMRegressionModel]): LightGBMRegressionModel
    Definition Classes
    Model
  68. def setPredictDisableShapeCheck(value: Boolean): LightGBMRegressionModel.this.type
    Definition Classes
    LightGBMPredictionParams
  69. def setPredictionCol(value: String): LightGBMRegressionModel
    Definition Classes
    PredictionModel
  70. def setStartIteration(value: Int): LightGBMRegressionModel.this.type
    Definition Classes
    LightGBMModelParams
  71. val startIteration: IntParam
    Definition Classes
    LightGBMModelParams
  72. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  73. def transform(dataset: Dataset[_]): DataFrame

    Adds additional Leaf Index and SHAP columns if specified.

    Adds additional Leaf Index and SHAP columns if specified.

    dataset

    input dataset

    returns

    transformed dataset

    Definition Classes
    LightGBMRegressionModel → PredictionModel → Transformer
  74. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  75. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  76. def transformSchema(schema: StructType): StructType
    Definition Classes
    PredictionModel → PipelineStage
  77. val uid: String
    Definition Classes
    LightGBMRegressionModel → SynapseMLLogging → Identifiable
  78. def write: MLWriter
    Definition Classes
    ComplexParamsWritable → MLWritable