# MetaMulticlassTrainer<TTransformer,TModel> Class

## Definition

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
MetaMulticlassTrainer<TTransformer,TModel>
Derived
Implements

## Methods

 Fits the data to the trainer. Gets the output columns.

## 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.