# ISingleFeaturePredictionTransformer<TModel> Interface

## Definition

An ISingleFeaturePredictionTransformer contains the name of the FeatureColumnName and its type, FeatureColumnType. Implementations of this interface, have the ability to score the data of an input IDataView through the Transform(IDataView)

public interface ISingleFeaturePredictionTransformer<out TModel> : Microsoft.ML.IPredictionTransformer<out TModel>, Microsoft.ML.ITransformer where TModel : class
type ISingleFeaturePredictionTransformer<'Model (requires 'Model : null)> = interface
interface IPredictionTransformer<'Model (requires 'Model : null)>
interface ITransformer
interface ICanSaveModel
Public Interface ISingleFeaturePredictionTransformer(Of Out TModel)
Implements IPredictionTransformer(Of Out TModel), ITransformer

#### Type Parameters

TModel

The Microsoft.ML.IPredictor or ICalibrator used for the data transformation.

Derived
Implements

## Properties

 The name of the feature column. Holds information about the type of the feature column. Whether a call to GetRowToRowMapper(DataViewSchema) should succeed, on an appropriate schema. (Inherited from ITransformer) (Inherited from IPredictionTransformer)

## Methods

 Schema propagation for transformers. Returns the output schema of the data, if the input schema is like the one provided. (Inherited from ITransformer) Constructs a row-to-row mapper based on an input schema. If IsRowToRowMapper is false, then an exception should be thrown. If the input schema is in any way unsuitable for constructing the mapper, an exception should likewise be thrown. (Inherited from ITransformer) (Inherited from ICanSaveModel) Take the data in, make transformations, output the data. Note that IDataView's are lazy, so no actual transformations happen here, just schema validation. (Inherited from ITransformer)

## Extension Methods

 Preview an effect of the transformer on a given data. Create a new transformer chain, by appending another transformer to the end of this transformer chain. TimeSeriesPredictionEngine creates a prediction engine for a time series pipeline. It updates the state of time series model with observations seen at prediction phase and allows checkpointing the model.