MetaMulticlassTrainer<TTransformer,TModel> Class

Definition

Namespace:

Microsoft.ML.Trainers

Assembly:

Microsoft.ML.StandardTrainers.dll

Package:

Microsoft.ML v3.0.1

public abstract class MetaMulticlassTrainer<TTransformer,TModel> : Microsoft.ML.IEstimator<TTransformer>, Microsoft.ML.Trainers.ITrainerEstimator<TTransformer,TModel> where TTransformer : ISingleFeaturePredictionTransformer<TModel> where TModel : class

type MetaMulticlassTrainer<'ransformer, 'Model (requires 'ransformer :> ISingleFeaturePredictionTransformer<'Model> and 'Model : null)> = class
    interface ITrainerEstimator<'ransformer, 'Model (requires 'ransformer :> ISingleFeaturePredictionTransformer<'Model> and 'Model : null)>
    interface IEstimator<'ransformer (requires 'ransformer :> ISingleFeaturePredictionTransformer<'Model>)>

Public MustInherit Class MetaMulticlassTrainer(Of TTransformer, TModel)
Implements IEstimator(Of TTransformer), ITrainerEstimator(Of TTransformer, TModel)

Type Parameters

TTransformer

TModel

Inheritance

Object

MetaMulticlassTrainer<TTransformer,TModel>

Derived

Microsoft.ML.Trainers.OneVersusAllTrainer

Microsoft.ML.Trainers.PairwiseCouplingTrainer

Implements

IEstimator<TTransformer> ITrainerEstimator<TTransformer,TModel>

Properties

Info

Methods

Fit(IDataView)	Fits the data to the trainer.
GetOutputSchema(SchemaShape)	Gets the output columns.

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.