class LightGBMClassificationModel extends ProbabilisticClassificationModel[Vector, LightGBMClassificationModel] with LightGBMModelParams with LightGBMModelMethods with LightGBMPredictionParams with HasActualNumClasses with ComplexParamsWritable with SynapseMLLogging

Model produced by LightGBMClassifier.

Linear Supertypes
SynapseMLLogging, ComplexParamsWritable, MLWritable, HasActualNumClasses, LightGBMPredictionParams, LightGBMModelMethods, LightGBMModelParams, Wrappable, DotnetWrappable, RWrappable, PythonWrappable, BaseWrappable, ProbabilisticClassificationModel[Vector, LightGBMClassificationModel], ProbabilisticClassifierParams, HasThresholds, HasProbabilityCol, ClassificationModel[Vector, LightGBMClassificationModel], ClassifierParams, HasRawPredictionCol, PredictionModel[Vector, LightGBMClassificationModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Model[LightGBMClassificationModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. LightGBMClassificationModel
  2. SynapseMLLogging
  3. ComplexParamsWritable
  4. MLWritable
  5. HasActualNumClasses
  6. LightGBMPredictionParams
  7. LightGBMModelMethods
  8. LightGBMModelParams
  9. Wrappable
  10. DotnetWrappable
  11. RWrappable
  12. PythonWrappable
  13. BaseWrappable
  14. ProbabilisticClassificationModel
  15. ProbabilisticClassifierParams
  16. HasThresholds
  17. HasProbabilityCol
  18. ClassificationModel
  19. ClassifierParams
  20. HasRawPredictionCol
  21. PredictionModel
  22. PredictorParams
  23. HasPredictionCol
  24. HasFeaturesCol
  25. HasLabelCol
  26. Model
  27. Transformer
  28. PipelineStage
  29. Logging
  30. Params
  31. Serializable
  32. Serializable
  33. Identifiable
  34. AnyRef
  35. Any
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Visibility
  1. Public
  2. All

Instance Constructors

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

Value Members

  1. val actualNumClasses: IntParam
    Definition Classes
    HasActualNumClasses
  2. final def clear(param: Param[_]): LightGBMClassificationModel.this.type
    Definition Classes
    Params
  3. def copy(extra: ParamMap): LightGBMClassificationModel
    Definition Classes
    LightGBMClassificationModel → Model → Transformer → PipelineStage → Params
  4. def dotnetAdditionalMethods: String
    Definition Classes
    DotnetWrappable
  5. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  6. def explainParams(): String
    Definition Classes
    Params
  7. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  8. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  9. final val featuresCol: Param[String]
    Definition Classes
    HasFeaturesCol
  10. val featuresShapCol: Param[String]
    Definition Classes
    LightGBMPredictionParams
  11. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  12. def getActualNumClasses: Int
    Definition Classes
    HasActualNumClasses
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  19. 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
  20. 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
  21. 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
  22. final def getFeaturesCol: String
    Definition Classes
    HasFeaturesCol
  23. def getFeaturesShapCol: String
    Definition Classes
    LightGBMPredictionParams
  24. final def getLabelCol: String
    Definition Classes
    HasLabelCol
  25. def getLeafPredictionCol: String
    Definition Classes
    LightGBMPredictionParams
  26. def getLightGBMBooster: LightGBMBooster
    Definition Classes
    LightGBMModelParams
  27. def getModel: LightGBMBooster

    Alias for same method

    Alias for same method

    returns

    The LightGBM Booster.

    Definition Classes
    LightGBMModelParams
  28. def getNativeModel(): String

    Gets the native model serialized representation as a string.

    Gets the native model serialized representation as a string.

    Definition Classes
    LightGBMModelMethods
  29. def getNumIterations: Int
    Definition Classes
    LightGBMModelParams
  30. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  31. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  32. def getParamInfo(p: Param[_]): ParamInfo[_]
    Definition Classes
    BaseWrappable
  33. def getPredictDisableShapeCheck: Boolean
    Definition Classes
    LightGBMPredictionParams
  34. final def getPredictionCol: String
    Definition Classes
    HasPredictionCol
  35. final def getProbabilityCol: String
    Definition Classes
    HasProbabilityCol
  36. final def getRawPredictionCol: String
    Definition Classes
    HasRawPredictionCol
  37. 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
  38. def getStartIteration: Int
    Definition Classes
    LightGBMModelParams
  39. def getThresholds: Array[Double]
    Definition Classes
    HasThresholds
  40. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  41. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  42. def hasParent: Boolean
    Definition Classes
    Model
  43. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  44. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  45. final val labelCol: Param[String]
    Definition Classes
    HasLabelCol
  46. val leafPredictionCol: Param[String]
    Definition Classes
    LightGBMPredictionParams
  47. val lightGBMBooster: LightGBMBoosterParam
    Definition Classes
    LightGBMModelParams
  48. def logClass(featureName: String): Unit
    Definition Classes
    SynapseMLLogging
  49. def logFit[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  50. def logTransform[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  51. def logVerb[T](verb: String, f: ⇒ T, columns: Option[Int] = None): T
    Definition Classes
    SynapseMLLogging
  52. def makeDotnetFile(conf: CodegenConfig): Unit
    Definition Classes
    DotnetWrappable
  53. def makePyFile(conf: CodegenConfig): Unit
    Definition Classes
    PythonWrappable
  54. def makeRFile(conf: CodegenConfig): Unit
    Definition Classes
    RWrappable
  55. def numClasses: Int
    Definition Classes
    LightGBMClassificationModel → ClassificationModel
  56. def numFeatures: Int
    Definition Classes
    PredictionModel
    Annotations
    @Since( "1.6.0" )
  57. val numIterations: IntParam
    Definition Classes
    LightGBMModelParams
  58. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  59. var parent: Estimator[LightGBMClassificationModel]
    Definition Classes
    Model
  60. def predict(features: Vector): Double
    Definition Classes
    ClassificationModel → PredictionModel
  61. val predictDisableShapeCheck: BooleanParam
    Definition Classes
    LightGBMPredictionParams
  62. def predictProbability(features: Vector): Vector
    Definition Classes
    LightGBMClassificationModel → ProbabilisticClassificationModel
  63. def predictRaw(features: Vector): Vector
    Definition Classes
    LightGBMClassificationModel → ClassificationModel
  64. final val predictionCol: Param[String]
    Definition Classes
    HasPredictionCol
  65. final val probabilityCol: Param[String]
    Definition Classes
    HasProbabilityCol
  66. def pyAdditionalMethods: String
    Definition Classes
    PythonWrappable
  67. def pyInitFunc(): String
    Definition Classes
    PythonWrappable
  68. final val rawPredictionCol: Param[String]
    Definition Classes
    HasRawPredictionCol
  69. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  70. 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
  71. final def set[T](param: Param[T], value: T): LightGBMClassificationModel.this.type
    Definition Classes
    Params
  72. def setActualNumClasses(value: Int): LightGBMClassificationModel.this.type
    Definition Classes
    HasActualNumClasses
  73. def setFeaturesCol(value: String): LightGBMClassificationModel
    Definition Classes
    PredictionModel
  74. def setFeaturesShapCol(value: String): LightGBMClassificationModel.this.type
    Definition Classes
    LightGBMPredictionParams
  75. def setLeafPredictionCol(value: String): LightGBMClassificationModel.this.type
    Definition Classes
    LightGBMPredictionParams
  76. def setLightGBMBooster(value: LightGBMBooster): LightGBMClassificationModel.this.type
    Definition Classes
    LightGBMModelParams
  77. def setNumIterations(value: Int): LightGBMClassificationModel.this.type
    Definition Classes
    LightGBMModelParams
  78. def setParent(parent: Estimator[LightGBMClassificationModel]): LightGBMClassificationModel
    Definition Classes
    Model
  79. def setPredictDisableShapeCheck(value: Boolean): LightGBMClassificationModel.this.type
    Definition Classes
    LightGBMPredictionParams
  80. def setPredictionCol(value: String): LightGBMClassificationModel
    Definition Classes
    PredictionModel
  81. def setProbabilityCol(value: String): LightGBMClassificationModel
    Definition Classes
    ProbabilisticClassificationModel
  82. def setRawPredictionCol(value: String): LightGBMClassificationModel
    Definition Classes
    ClassificationModel
  83. def setStartIteration(value: Int): LightGBMClassificationModel.this.type
    Definition Classes
    LightGBMModelParams
  84. def setThresholds(value: Array[Double]): LightGBMClassificationModel
    Definition Classes
    ProbabilisticClassificationModel
  85. val startIteration: IntParam
    Definition Classes
    LightGBMModelParams
  86. val thresholds: DoubleArrayParam
    Definition Classes
    HasThresholds
  87. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  88. def transform(dataset: Dataset[_]): DataFrame

    Implementation based on ProbabilisticClassifier with slight modifications to avoid calling raw2probabilityInPlace to defer the probability calculation to lightgbm native code.

    Implementation based on ProbabilisticClassifier with slight modifications to avoid calling raw2probabilityInPlace to defer the probability calculation to lightgbm native code.

    dataset

    input dataset

    returns

    transformed dataset

    Definition Classes
    LightGBMClassificationModel → ProbabilisticClassificationModel → ClassificationModel → PredictionModel → Transformer
  89. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  90. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  91. final def transformImpl(dataset: Dataset[_]): DataFrame
    Definition Classes
    ClassificationModel → PredictionModel
  92. def transformSchema(schema: StructType): StructType
    Definition Classes
    ProbabilisticClassificationModel → ClassificationModel → PredictionModel → PipelineStage
  93. val uid: String
    Definition Classes
    LightGBMClassificationModel → SynapseMLLogging → Identifiable
  94. def write: MLWriter
    Definition Classes
    ComplexParamsWritable → MLWritable