# LightGbmMulticlassTrainer Class

## Definition

The IEstimator<TTransformer> for training a boosted decision tree multi-class classification model using LightGBM.

public sealed class LightGbmMulticlassTrainer : Microsoft.ML.Trainers.LightGbm.LightGbmTrainerBase<Microsoft.ML.Trainers.LightGbm.LightGbmMulticlassTrainer.Options,Microsoft.ML.Data.VBuffer<float>,Microsoft.ML.Data.MulticlassPredictionTransformer<Microsoft.ML.Trainers.OneVersusAllModelParameters>,Microsoft.ML.Trainers.OneVersusAllModelParameters>
type LightGbmMulticlassTrainer = class
inherit LightGbmTrainerBase<LightGbmMulticlassTrainer.Options, VBuffer<single>, MulticlassPredictionTransformer<OneVersusAllModelParameters>, OneVersusAllModelParameters>
Public NotInheritable Class LightGbmMulticlassTrainer
Inherits LightGbmTrainerBase(Of LightGbmMulticlassTrainer.Options, VBuffer(Of Single), MulticlassPredictionTransformer(Of OneVersusAllModelParameters), OneVersusAllModelParameters)
Inheritance

## Remarks

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

### Input and Output Columns

The input label column data must be key type and the feature column must be a known-sized vector of Single.

This trainer outputs the following columns:

Output Column Name Column Type Description
Score Vector of Single The scores of all classes. Higher value means higher probability to fall into the associated class. If the i-th element has the largest value, the predicted label index would be i. Note that i is zero-based index.
PredictedLabel key type The predicted label's index. If its value is i, the actual label would be the i-th category in the key-valued input label type.

### Trainer Characteristics

Machine learning task Multiclass classification
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

 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)

## Properties

 (Inherited from LightGbmTrainerBase)

## Methods

 Trains and returns a ITransformer. (Inherited from TrainerEstimatorBase) Trains a LightGbmMulticlassTrainer using both training and validation data, returns a MulticlassPredictionTransformer. (Inherited from TrainerEstimatorBase)

## Extension Methods

 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. 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.