SdcaBinaryTrainerBase<TModelParameters> Class

Definition

Namespace:: Microsoft.ML.Trainers

Assembly:: Microsoft.ML.StandardTrainers.dll

Package:: Microsoft.ML v4.0.1

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 v2.0.1

Package:: Microsoft.ML v3.0.1

Package:: Microsoft.ML v5.0.0-preview.1.25125.4

Source:: SdcaBinary.cs

Source:: SdcaBinary.cs

Source:: SdcaBinary.cs

Important

Some information relates to prerelease product that may be substantially modified before it’s released. Microsoft makes no warranties, express or implied, with respect to the information provided here.

SDCA is a general training algorithm for (generalized) linear models such as support vector machine, linear regression, logistic regression, and so on. SDCA binary classification trainer family includes several sealed members: (1) SdcaNonCalibratedBinaryTrainer supports general loss functions and returns LinearBinaryModelParameters. (2) SdcaLogisticRegressionBinaryTrainer essentially trains a regularized logistic regression model. Because logistic regression naturally provide probability output, this generated model's type is CalibratedModelParametersBase<TSubModel,TCalibrator>. where TSubModel is LinearBinaryModelParameters and TCalibrator is PlattCalibrator.

public abstract class SdcaBinaryTrainerBase<TModelParameters> : Microsoft.ML.Trainers.SdcaTrainerBase<Microsoft.ML.Trainers.SdcaBinaryTrainerBase<TModelParameters>.BinaryOptionsBase,Microsoft.ML.Data.BinaryPredictionTransformer<TModelParameters>,TModelParameters> where TModelParameters : class

type SdcaBinaryTrainerBase<'ModelParameters (requires 'ModelParameters : null)> = class
    inherit SdcaTrainerBase<SdcaBinaryTrainerBase<'ModelParameters>.BinaryOptionsBase, BinaryPredictionTransformer<'ModelParameters>, 'ModelParameters (requires 'ModelParameters : null)>

Public MustInherit Class SdcaBinaryTrainerBase(Of TModelParameters)
Inherits SdcaTrainerBase(Of SdcaBinaryTrainerBase(Of TModelParameters).BinaryOptionsBase, BinaryPredictionTransformer(Of TModelParameters), TModelParameters)

Type Parameters

TModelParameters

Inheritance: Object

TrainerEstimatorBase<TTransformer,TModel>

StochasticTrainerBase<TTransformer,TModel>

SdcaTrainerBase<SdcaBinaryTrainerBase<TModelParameters>.BinaryOptionsBase,BinaryPredictionTransformer<TModelParameters>,TModelParameters>
SdcaBinaryTrainerBase<TModelParameters>

Derived: Microsoft.ML.Trainers.SdcaLogisticRegressionBinaryTrainer

Microsoft.ML.Trainers.SdcaNonCalibratedBinaryTrainer

Fields

Name	Description
FeatureColumn	The feature column that the trainer expects. (Inherited from TrainerEstimatorBase<TTransformer,TModel>)
LabelColumn	The label column that the trainer expects. Can be `null`, which indicates that label is not used for training. (Inherited from TrainerEstimatorBase<TTransformer,TModel>)
WeightColumn	The weight column that the trainer expects. Can be `null`, which indicates that weight is not used for training. (Inherited from TrainerEstimatorBase<TTransformer,TModel>)

Properties

Name	Description
Info

Methods

Name	Description
Fit(IDataView)	Trains and returns a ITransformer. (Inherited from TrainerEstimatorBase<TTransformer,TModel>)
GetOutputSchema(SchemaShape)	(Inherited from TrainerEstimatorBase<TTransformer,TModel>)

Extension Methods

Name	Description
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.

Name

Description

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.

Applies to