package lightgbm
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Type Members
- case class ColumnParams(labelColumn: String, featuresColumn: String, weightColumn: Option[String], initScoreColumn: Option[String], groupColumn: Option[String]) extends Product with Serializable
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trait
HasActualNumClasses extends Params
Special parameter for classification model for actual number of classes in dataset
- trait LightGBMBase[TrainedModel <: Model[TrainedModel]] extends Estimator[TrainedModel] with LightGBMParams with HasFeaturesCol with HasLabelCol with BasicLogging
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class
LightGBMClassificationModel extends ProbabilisticClassificationModel[Vector, LightGBMClassificationModel] with LightGBMModelParams with LightGBMModelMethods with LightGBMPredictionParams with HasActualNumClasses with ComplexParamsWritable with BasicLogging
Model produced by LightGBMClassifier.
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class
LightGBMClassifier extends ProbabilisticClassifier[Vector, LightGBMClassifier, LightGBMClassificationModel] with LightGBMBase[LightGBMClassificationModel] with BasicLogging
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
- trait LightGBMDelegate extends Serializable
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trait
LightGBMModelMethods extends LightGBMModelParams with Logging
Contains common LightGBM model methods across all LightGBM learner types.
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class
LightGBMRanker extends Ranker[Vector, LightGBMRanker, LightGBMRankerModel] with LightGBMBase[LightGBMRankerModel] with BasicLogging
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
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class
LightGBMRankerModel extends RankerModel[Vector, LightGBMRankerModel] with LightGBMModelParams with LightGBMModelMethods with LightGBMPredictionParams with ComplexParamsWritable with BasicLogging
Model produced by LightGBMRanker.
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class
LightGBMRegressionModel extends RegressionModel[Vector, LightGBMRegressionModel] with LightGBMModelParams with LightGBMModelMethods with LightGBMPredictionParams with ComplexParamsWritable with BasicLogging
Model produced by LightGBMRegressor.
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class
LightGBMRegressor extends BaseRegressor[Vector, LightGBMRegressor, LightGBMRegressionModel] with LightGBMBase[LightGBMRegressionModel] with BasicLogging
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
- case class NetworkParams(defaultListenPort: Int, addr: String, port: Int, barrierExecutionMode: Boolean) extends Product with Serializable
- class SharedState extends AnyRef
Value Members
-
object
ConnectionState extends Enumeration
Connection state of a worker
- object LightGBMClassificationModel extends ComplexParamsReadable[LightGBMClassificationModel] with Serializable
- object LightGBMClassifier extends DefaultParamsReadable[LightGBMClassifier] with Serializable
- object LightGBMConstants
- object LightGBMRanker extends DefaultParamsReadable[LightGBMRanker] with Serializable
- object LightGBMRankerModel extends ComplexParamsReadable[LightGBMRankerModel] with Serializable
- object LightGBMRegressionModel extends ComplexParamsReadable[LightGBMRegressionModel] with Serializable
- object LightGBMRegressor extends DefaultParamsReadable[LightGBMRegressor] with Serializable
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object
LightGBMUtils
Helper utilities for LightGBM learners
- object TaskTrainingMethods