HashingTransformer Class

Reference

Definition

Namespace:: Microsoft.ML.Transforms

Assembly:: Microsoft.ML.Data.dll

Package:: Microsoft.ML v3.0.1

Package:: Microsoft.ML v1.0.0

Package:: Microsoft.ML v1.1.0

Package:: Microsoft.ML v1.2.0

Package:: Microsoft.ML v1.3.1

Package:: Microsoft.ML v1.4.0

Package:: Microsoft.ML v1.5.5

Package:: Microsoft.ML v1.6.0

Package:: Microsoft.ML v1.7.0

Package:: Microsoft.ML v2.0.0

Important

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ITransformer resulting from fitting a HashingEstimator.

public sealed class HashingTransformer : Microsoft.ML.Data.OneToOneTransformerBase

type HashingTransformer = class
    inherit OneToOneTransformerBase

Public NotInheritable Class HashingTransformer
Inherits OneToOneTransformerBase

Inheritance: Object

RowToRowTransformerBase

OneToOneTransformerBase
HashingTransformer

Methods

GetOutputSchema(DataViewSchema)	(Inherited from RowToRowTransformerBase)
Transform(IDataView)	(Inherited from RowToRowTransformerBase)

Explicit Interface Implementations

ICanSaveModel.Save(ModelSaveContext)	(Inherited from RowToRowTransformerBase)
ITransformer.GetRowToRowMapper(DataViewSchema)	(Inherited from RowToRowTransformerBase)
ITransformer.IsRowToRowMapper	(Inherited from RowToRowTransformerBase)

Extension Methods

Preview(ITransformer, IDataView, Int32)	Preview an effect of the `transformer` on a given `data`.
Append<TTrans>(ITransformer, TTrans)	Create a new transformer chain, by appending another transformer to the end of this transformer chain.
CreateTimeSeriesEngine<TSrc,TDst>(ITransformer, IHostEnvironment, PredictionEngineOptions)	TimeSeriesPredictionEngine<TSrc,TDst> 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.
CreateTimeSeriesEngine<TSrc,TDst>(ITransformer, IHostEnvironment, Boolean, SchemaDefinition, SchemaDefinition)	TimeSeriesPredictionEngine<TSrc,TDst> 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.

Applies to

Dela via