SdcaBinaryTrainerBase<TModelParameters> Class

Definition

Namespace:

Microsoft.ML.Trainers

Assembly:

Microsoft.ML.StandardTrainers.dll

Package:

Microsoft.ML v3.0.1

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

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

Info

Methods

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

Extension Methods

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.