LightGbmMulticlassTrainer Class
Definition
Important
Some information relates to prerelease product that may be substantially modified before it’s released. Microsoft makes no warranties, express or implied, with respect to the information provided here.
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
| 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 |
| WeightColumn |
The weight column that the trainer expects. Can be |
Properties
| Info | (Inherited from LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>) |
Methods
| Fit(IDataView, IDataView) |
Trains a LightGbmMulticlassTrainer using both training and validation data, returns a MulticlassPredictionTransformer<TModel>. |
| Fit(IDataView) |
Trains and returns a ITransformer. (Inherited from TrainerEstimatorBase<TTransformer,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
- LightGbm(MulticlassClassificationCatalog+MulticlassClassificationTrainers, String, String, String, Nullable<Int32>, Nullable<Int32>, Nullable<Double>, Int32)
- LightGbm(MulticlassClassificationCatalog+MulticlassClassificationTrainers, LightGbmMulticlassTrainer+Options)
- LightGbmMulticlassTrainer.Options