lightgbm

Type Members

1. class BoosterHandler extends AnyRef

   

   Wraps the boosterPtr and guarantees that Native library is initialized everytime it is needed

   Wraps the boosterPtr and guarantees that Native library is initialized everytime it is needed

   Attributes

   protected

2. case class ClassifierTrainParams(parallelism: String, topK: Int, numIterations: Int, learningRate: Double, numLeaves: Int, 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, objective: String, modelString: Option[String], isUnbalance: Boolean, verbosity: Int, categoricalFeatures: Array[Int], numClass: 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 classifier.

3. case class ColumnParams(labelColumn: String, featuresColumn: String, weightColumn: Option[String], initScoreColumn: Option[String], groupColumn: Option[String]) extends Product with Serializable

   

4. class DoubleNativePtrHandler extends NativePtrHandler[SWIGTYPE_p_double]

   

   Attributes

   protected

5. trait HasActualNumClasses extends Params

   

   Special parameter for classification model for actual number of classes in dataset

6. trait LightGBMBase[TrainedModel <: Model[TrainedModel]] extends Estimator[TrainedModel] with LightGBMParams with HasFeaturesCol with HasLabelCol

   

7. trait LightGBMBinParams extends Wrappable

   

   Defines common parameters across all LightGBM learners related to histogram bin construction.

8. class LightGBMBooster extends Serializable

   

   Represents a LightGBM Booster learner

   Represents a LightGBM Booster learner

   Annotations

   @SerialVersionUID()

9. class LightGBMBoosterParam extends ComplexParam[LightGBMBooster]

   

   Custom ComplexParam for LightGBMBooster, to make it settable on the LightGBM models.

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

    

    Model produced by LightGBMClassifier.

    Model produced by LightGBMClassifier.

    Annotations

    @InternalWrapper()

11. class LightGBMClassifier extends ProbabilisticClassifier[Vector, LightGBMClassifier, LightGBMClassificationModel] with LightGBMBase[LightGBMClassificationModel]

    

    Trains a LightGBM Classification model, a fast, distributed, high performance gradient boosting framework based on decision tree algorithms.

    Trains a LightGBM Classification model, a fast, distributed, high performance gradient boosting framework based on decision tree algorithms. For more information please see here: https://github.com/Microsoft/LightGBM. For parameter information see here: https://github.com/Microsoft/LightGBM/blob/master/docs/Parameters.rst

    Annotations

    @InternalWrapper()

12. class LightGBMDataset extends AutoCloseable

    

    Represents a LightGBM dataset.

    Represents a LightGBM dataset. Wraps the native implementation.

13. trait LightGBMDelegate extends Serializable

    

14. trait LightGBMExecutionParams extends Wrappable

    

    Defines common LightGBM execution parameters.

15. trait LightGBMFractionParams extends Wrappable

    

    Defines parameters for fraction across all LightGBM learners.

16. trait LightGBMLearnerParams extends Wrappable

    

    Defines common parameters across all LightGBM learners related to learning score evolution.

17. trait LightGBMModelMethods extends LightGBMModelParams

    

    Contains common LightGBM model methods across all LightGBM learner types.

18. trait LightGBMModelParams extends Wrappable

    

    Defines parameters for LightGBM models

19. trait LightGBMParams extends Wrappable with DefaultParamsWritable with HasWeightCol with HasValidationIndicatorCol with HasInitScoreCol with LightGBMExecutionParams with LightGBMSlotParams with LightGBMFractionParams with LightGBMBinParams with LightGBMLearnerParams with LightGBMPredictionParams

    

    Defines common parameters across all LightGBM learners.

20. trait LightGBMPredictionParams extends Wrappable

    

    Defines common prediction parameters across LightGBM Ranker, Classifier and Regressor

21. class LightGBMRanker extends Ranker[Vector, LightGBMRanker, LightGBMRankerModel] with LightGBMBase[LightGBMRankerModel]

    

    Trains a LightGBMRanker model, a fast, distributed, high performance gradient boosting framework based on decision tree algorithms.

    Trains a LightGBMRanker model, a fast, distributed, high performance gradient boosting framework based on decision tree algorithms. For more information please see here: https://github.com/Microsoft/LightGBM. For parameter information see here: https://github.com/Microsoft/LightGBM/blob/master/docs/Parameters.rst

    Annotations

    @InternalWrapper()

22. class LightGBMRankerModel extends RankerModel[Vector, LightGBMRankerModel] with LightGBMModelParams with LightGBMModelMethods with LightGBMPredictionParams with ComplexParamsWritable

    

    Model produced by LightGBMRanker.

    Model produced by LightGBMRanker.

    Annotations

    @InternalWrapper()

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

    

    Model produced by LightGBMRegressor.

    Model produced by LightGBMRegressor.

    Annotations

    @InternalWrapper()

24. class LightGBMRegressor extends BaseRegressor[Vector, LightGBMRegressor, LightGBMRegressionModel] with LightGBMBase[LightGBMRegressionModel]

    

    Trains a LightGBM Regression model, a fast, distributed, high performance gradient boosting framework based on decision tree algorithms.

    Trains a LightGBM Regression model, a fast, distributed, high performance gradient boosting framework based on decision tree algorithms. For more information please see here: https://github.com/Microsoft/LightGBM. For parameter information see here: https://github.com/Microsoft/LightGBM/blob/master/docs/Parameters.rst Note: The application parameter supports the following values:

    • regression_l2, L2 loss, alias=regression, mean_squared_error, mse, l2_root, root_mean_squared_error, rmse
    • regression_l1, L1 loss, alias=mean_absolute_error, mae
    • huber, Huber loss
    • fair, Fair loss
    • poisson, Poisson regression
    • quantile, Quantile regression
    • mape, MAPE loss, alias=mean_absolute_percentage_error
    • gamma, Gamma regression with log-link. It might be useful, e.g., for modeling insurance claims severity, or for any target that might be gamma-distributed
    • tweedie, Tweedie regression with log-link. It might be useful, e.g., for modeling total loss in insurance, or for any target that might be tweedie-distributed

    Annotations

    @InternalWrapper()

25. trait LightGBMSlotParams extends Wrappable

    

    Defines parameters for slots across all LightGBM learners.

26. class LongLongNativePtrHandler extends NativePtrHandler[SWIGTYPE_p_long_long]

    

    Attributes

    protected

27. abstract class NativePtrHandler[T] extends AnyRef

    

    Attributes

    protected

28. case class NetworkParams(defaultListenPort: Int, addr: String, port: Int, barrierExecutionMode: Boolean) extends Product with Serializable

    

29. 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.

30. 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.

31. abstract class TrainParams extends Serializable

    

    Defines the common Booster parameters passed to the LightGBM learners.