# CustomMappingEstimator<TSrc,TDst> Class

## Definition

Applies a custom mapping function to the specified input columns. The result will be in output columns.

public sealed class CustomMappingEstimator<TSrc,TDst> : Microsoft.ML.Data.TrivialEstimator<Microsoft.ML.Transforms.CustomMappingTransformer<TSrc,TDst>> where TSrc : class, new() where TDst : class, new()
type CustomMappingEstimator<'Src, 'Dst (requires 'Src : null and 'Src : (new : unit -> 'Src) and 'Dst : null and 'Dst : (new : unit -> 'Dst))> = class
inherit TrivialEstimator<CustomMappingTransformer<'Src, 'Dst>>
Public NotInheritable Class CustomMappingEstimator(Of TSrc, TDst)
Inherits TrivialEstimator(Of CustomMappingTransformer(Of TSrc, TDst))

#### Type Parameters

TSrc
TDst
Inheritance
CustomMappingEstimator<TSrc,TDst>

## Remarks

### Estimator Characteristics

Does this estimator need to look at the data to train its parameters? No
Input column data type Any
Output column data type Any

The resulting CustomMappingTransformer<TSrc,TDst> applies a user defined mapping to one or more input columns and produces one or more output columns. This transformation doesn't change the number of rows, and can be seen as the result of applying the user's function to every row of the input data.

The provided custom function must be thread-safe and free from side effects. The order with which it is applied to the rows of data cannot be guaranteed.

Check the See Also section for links to usage examples.

## Methods

 (Inherited from TrivialEstimator) Returns the SchemaShape of the schema which will be produced by the transformer. Used for schema propagation and verification in a pipeline.

## 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.