# LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel> Class

## Definition

Base class for all training with LightGBM.

public abstract class LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel> : Microsoft.ML.Trainers.TrainerEstimatorBaseWithGroupId<TTransformer,TModel> where TOptions : LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>.OptionsBasenew() where TTransformer : ISingleFeaturePredictionTransformer<TModel> where TModel : class
type LightGbmTrainerBase<'Options, 'Output, 'ransformer, 'Model (requires 'Options :> LightGbmTrainerBase<'Options, 'Output, 'ransformer, 'Model>.OptionsBase and 'Options : (new : unit -> 'Options) and 'ransformer :> ISingleFeaturePredictionTransformer<'Model> and 'Model : null)> = class
inherit TrainerEstimatorBaseWithGroupId<'ransformer, 'Model (requires 'ransformer :> ISingleFeaturePredictionTransformer<'Model> and 'Model : null)>
Public MustInherit Class LightGbmTrainerBase(Of TOptions, TOutput, TTransformer, TModel)
Inherits TrainerEstimatorBaseWithGroupId(Of TTransformer, TModel)

#### Type Parameters

TOptions
TOutput
TTransformer
TModel
Inheritance
LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>
Derived

## Fields

 The feature column that the trainer expects. (Inherited from TrainerEstimatorBase) The optional groupID column that the ranking trainers expects. (Inherited from TrainerEstimatorBaseWithGroupId) The label column that the trainer expects. Can be null, which indicates that label is not used for training. (Inherited from TrainerEstimatorBase) The weight column that the trainer expects. Can be null, which indicates that weight is not used for training. (Inherited from TrainerEstimatorBase)

## Methods

 Trains and returns a ITransformer. (Inherited from TrainerEstimatorBase) (Inherited from TrainerEstimatorBase)

## Extension Methods

 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 are often formed into pipelines with many objects, so we may need to build a chain of estimators via EstimatorChain 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.