LightGbmRegressionTrainer Class

  • Reference

Definition

Namespace:
Microsoft.ML.Trainers.LightGbm
Assembly:
Microsoft.ML.LightGbm.dll
Package:
Microsoft.ML.LightGbm v3.0.1
Package:
Microsoft.ML.LightGbm v1.0.0
Package:
Microsoft.ML.LightGbm v1.1.0
Package:
Microsoft.ML.LightGbm v1.2.0
Package:
Microsoft.ML.LightGbm v1.3.1
Package:
Microsoft.ML.LightGbm v1.4.0
Package:
Microsoft.ML.LightGbm v1.5.5
Package:
Microsoft.ML.LightGbm v1.6.0
Package:
Microsoft.ML.LightGbm v1.7.0
Package:
Microsoft.ML.LightGbm v2.0.0

The IEstimator<TTransformer> for training a boosted decision tree regression model using LightGBM.

public sealed class LightGbmRegressionTrainer : Microsoft.ML.Trainers.LightGbm.LightGbmTrainerBase<Microsoft.ML.Trainers.LightGbm.LightGbmRegressionTrainer.Options,float,Microsoft.ML.Data.RegressionPredictionTransformer<Microsoft.ML.Trainers.LightGbm.LightGbmRegressionModelParameters>,Microsoft.ML.Trainers.LightGbm.LightGbmRegressionModelParameters>
type LightGbmRegressionTrainer = class
    inherit LightGbmTrainerBase<LightGbmRegressionTrainer.Options, single, RegressionPredictionTransformer<LightGbmRegressionModelParameters>, LightGbmRegressionModelParameters>
Public NotInheritable Class LightGbmRegressionTrainer
Inherits LightGbmTrainerBase(Of LightGbmRegressionTrainer.Options, Single, RegressionPredictionTransformer(Of LightGbmRegressionModelParameters), LightGbmRegressionModelParameters)
Inheritance
Object
TrainerEstimatorBase<TTransformer,TModel>
TrainerEstimatorBaseWithGroupId<TTransformer,TModel>
LightGbmTrainerBase<LightGbmRegressionTrainer.Options,Single,RegressionPredictionTransformer<LightGbmRegressionModelParameters>,LightGbmRegressionModelParameters>
LightGbmRegressionTrainer

Remarks

To create this trainer, use LightGbm or LightGbm(Options).

Input and Output Columns

The input label column data must be Single. The input features column data must be a known-sized vector of Single.

This trainer outputs the following columns:

Output Column Name Column Type Description
Score Single The unbounded score that was predicted by the model.

Trainer Characteristics
Machine learning task Regression
Is normalization required? No
Is caching required? No
Required NuGet in addition to Microsoft.ML Microsoft.ML.LightGbm
Exportable to ONNX Yes

Training Algorithm Details

LightGBM is an open source implementation of gradient boosting decision tree. For implementation details, please see LightGBM's official documentation or this paper.

Check the See Also section for links to examples of the usage.

Fields

FeatureColumn

The feature column that the trainer expects.

(Inherited from TrainerEstimatorBase<TTransformer,TModel>)
GroupIdColumn

The optional groupID column that the ranking trainers expects.

(Inherited from TrainerEstimatorBaseWithGroupId<TTransformer,TModel>)
LabelColumn

The label column that the trainer expects. Can be null, which indicates that label is not used for training.

(Inherited from TrainerEstimatorBase<TTransformer,TModel>)
WeightColumn

The weight column that the trainer expects. Can be null, which indicates that weight is not used for training.

(Inherited from TrainerEstimatorBase<TTransformer,TModel>)

Properties

Info (Inherited from LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>)

Methods

Fit(IDataView)

Trains and returns a ITransformer.

(Inherited from TrainerEstimatorBase<TTransformer,TModel>)
Fit(IDataView, IDataView)

Trains a LightGbmRegressionTrainer using both training and validation data, returns a RegressionPredictionTransformer<TModel>.
GetOutputSchema(SchemaShape) (Inherited from TrainerEstimatorBase<TTransformer,TModel>)

Extension Methods

AppendCacheCheckpoint<TTrans>(IEstimator<TTrans>, IHostEnvironment)

Append a 'caching checkpoint' to the estimator chain. This will ensure that the downstream estimators will be trained against cached data. It is helpful to have a caching checkpoint before trainers that take multiple data passes.
WithOnFitDelegate<TTransformer>(IEstimator<TTransformer>, Action<TTransformer>)

Given an estimator, return a wrapping object that will call a delegate once Fit(IDataView) is called. It is often important for an estimator to return information about what was fit, which is why the Fit(IDataView) method returns a specifically typed object, rather than just a general ITransformer. However, at the same time, IEstimator<TTransformer> are often formed into pipelines with many objects, so we may need to build a chain of estimators via EstimatorChain<TLastTransformer> where the estimator for which we want to get the transformer is buried somewhere in this chain. For that scenario, we can through this method attach a delegate that will be called once fit is called.

Applies to

See also