lightgbm

Type Members

1. case class ClassifierTrainParams(parallelism: String, numIterations: Int, learningRate: Double, numLeaves: Int, maxBin: Int, baggingFraction: Double, baggingFreq: Int, baggingSeed: Int, earlyStoppingRound: Int, 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) extends TrainParams with Product with Serializable

   

   Defines the Booster parameters passed to the LightGBM classifier.

2. trait HasFeatureImportanceGetters extends AnyRef

   

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

   

4. class LightGBMBooster extends Serializable

   

   Represents a LightGBM Booster learner

   Represents a LightGBM Booster learner

   Annotations

   @SerialVersionUID()

5. class LightGBMClassificationModel extends ProbabilisticClassificationModel[Vector, LightGBMClassificationModel] with HasFeatureImportanceGetters with ConstructorWritable[LightGBMClassificationModel]

   

   Model produced by LightGBMClassifier.

   Model produced by LightGBMClassifier.

   Annotations

   @InternalWrapper()

6. 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()

7. class LightGBMDataset extends AutoCloseable

   

   Represents a LightGBM dataset.

   Represents a LightGBM dataset. Wraps the native implementation.

8. trait LightGBMExecutionParams extends Wrappable

   

   Defines common LightGBM execution parameters.

9. trait LightGBMParams extends Wrappable with DefaultParamsWritable with HasWeightCol with HasValidationIndicatorCol with HasInitScoreCol with LightGBMExecutionParams

   

   Defines common parameters across all LightGBM learners.

10. 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()

11. class LightGBMRankerModel extends RankerModel[Vector, LightGBMRankerModel] with ConstructorWritable[LightGBMRankerModel]

    

    Model produced by LightGBMRanker.

    Model produced by LightGBMRanker.

    Annotations

    @InternalWrapper()

12. class LightGBMRegressionModel extends RegressionModel[Vector, LightGBMRegressionModel] with HasFeatureImportanceGetters with ConstructorWritable[LightGBMRegressionModel]

    

    Model produced by LightGBMRegressor.

    Model produced by LightGBMRegressor.

    Annotations

    @InternalWrapper()

13. 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()

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

    

15. case class RankerTrainParams(parallelism: String, numIterations: Int, learningRate: Double, numLeaves: Int, objective: String, maxBin: Int, baggingFraction: Double, baggingFreq: Int, baggingSeed: Int, earlyStoppingRound: Int, 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]) extends TrainParams with Product with Serializable

    

    Defines the Booster parameters passed to the LightGBM ranker.

16. case class RegressorTrainParams(parallelism: String, numIterations: Int, learningRate: Double, numLeaves: Int, objective: String, alpha: Double, tweedieVariancePower: Double, maxBin: Int, baggingFraction: Double, baggingFreq: Int, baggingSeed: Int, earlyStoppingRound: Int, 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) extends TrainParams with Product with Serializable

    

    Defines the Booster parameters passed to the LightGBM regressor.

17. abstract class TrainParams extends Serializable

    

    Defines the common Booster parameters passed to the LightGBM learners.